I
Application of attenuated total reflection
Fourier transform infrared (ATR-FTIR)
spectroscopy to measure sub-lethal effects of
potential mutagens.
A thesis submitted for the degree of Doctor of Philosophy in
the Faculty of Science and Technology, Lancaster University
Alternative format thesis
November, 2015
Blessing Ebele Obinaju
(Msc)
Lancaster Environment Centre
II
Abstract
Techniques employed in vibrational spectroscopy monitor the vibrational modes of
functional groups within biomolecules and enable a correlation between chemical
information and histological structures. Interrogation of biological samples using
infrared (IR) techniques generates spectrum with wavenumber-absorbance intensities
specific to biomolecules within the sample. Methods are relatively non-destructive,
and so samples can subsequently be analyzed by more conventional approaches.
Analyses can be carried out ex vivo or in situ in living tissue, where a reference range
of a designated normal state can be derived, and anything lying outside this range is
potentially atypical. Computational approaches allow one to minimize within-category
confounding factors. The application of vibrational spectroscopy in contaminant
biomonitoring is a welcome development which has enabled the investigation of real-
time contaminant exposure effects in the tissues of sentinels. IR techniques such as
attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, was
able to detect changes in various tissue samples exposed to varying levels of
polycyclic aromatic hydrocarbons (PAHs). This technique discriminated between
spatial and temporal variations in the interrogated tissues. Multivariate analysis was
able to relate the alterations at various regions of the fingerprint, to PAH exposure and
was able to detect PAH exposure in tissues from sites with no documented knowledge
of contamination. ATR-FTIR detected PAH-induced changes in isolated nuclei of
cultured cell populations in G0/G1 and S- phases of the cell cycle. Findings from the
various projects affirm, that techniques involved in IR spectroscopy are highly
sensitive to minimal changes in cell molecules. The ability to generate rapid results in
real-time is valuable and the wide variety of sample types which can be interrogated
using IR techniques makes it a suitable technique for environment biomonitoring.
III
Acknowledgements
I am extremely grateful to God for grace, health and sustenance during the entire
duration of the Ph.D. program. I am thankful to have had the support of my immediate
family, friends, and work colleagues both at Lancaster University and the University
of Uyo, as well as every individual who has contributed in words and by acts of
encouragement. I am grateful and honoured to have been a part of a group of inspiring
researchers; Rebecca Strong, Junyi Li, Holly Butler, Kelly Heys, Simon Forgarty,
Alana Mitchell, Georgios Theophilou and others who I had the pleasure of interacting
briefly with including Dr. Valon Llabjani, Dr. Imran Patel, Dr. Abdullah Ahmadzai,
Dr. Matt Riding and Dr. Julio Trevisan. Most importantly, I am extremely grateful to
my supervisor Prof. Francis Martin for supporting, guiding and encouraging me
throughout the entire Ph.D. I certainly would not have made it without you all.
Declaration
I declare that this thesis is my work and has not been submitted for the award of a
higher degree or qualification at this university or elsewhere
IV
Contents
Title page
I
Abstract
II
Acknowledgements
III
Declaration
III
Contents
IV
Abbreviations
V
Chapter 1.
General Introduction
1
Chapter 2.
Novel biospectroscopy sensor technologies towards
environmental health monitoring in urban
environments.
Blessing E. Obinaju and Francis L. Martin
Environmental Pollution 183 (2013) 46-53.
47
Chapter 3.
Distinguishing nuclei-specific benzo[a]pyrene-
induced effects in MCF-7 cells from whole-cell
alterations using Fourier-transform infrared
spectroscopy
Blessing E. Obinaju, Nigel J. Fullwood and Francis L.
Martin
Toxicology 335 (2015) 27–34
55
V
Chapter 4.
Novel sensor technologies towards environmental
health monitoring in urban environments: a case
study in the Niger Delta (Nigeria).
Blessing E. Obinaju, Alozie Alaoma and Francis L.
Martin
Environmental Pollution 192 (2014) 222-231.
78
Chapter 5.
Linking biochemical perturbations in tissues of the
African catfish to the presence of polycyclic
aromatic hydrocarbons in Ovia River, Niger Delta
region.
Blessing E. Obinaju, Carola Graf, Crispin Halsall and
Francis L. Martin
Environmental Pollution 201 (2015) 42-49
99
Chapter 6.
Attenuated total reflection Fourier-transform infrared
spectroscopy reveals polycyclic aromatic
hydrocarbon contamination despite relatively
pristine characteristics of site: results of a field study
in the Niger Delta
Blessing E. Obinaju and Francis L. Martin
Submitted Manuscript
115
Chapter 7.
Discussion
146
Appendix I
Using Fourier transform IR spectroscopy to analyze
biological materials
Matthew J. Baker, Júlio Trevisan, Paul Bassan, Rohit
Bhargava, Holly J. Butler, Konrad M. Dorling, Peter R.
Fielden, Simon W. Fogarty, Nigel J. Fullwood, Kelly A.
Heys, Caryn Hughes, Peter Lasch, Pierre L. Martin-
Hirsch, Blessing Obinaju, Ganesh D. Sockalingum,
Josep Sulé-Suso, Rebecca J. Strong, Michael J. Walsh,
Bayden R. Wood, Peter Gardner, Francis L. Martin,
Nature Protocols 9 (2014) 1771-1791.
157
VI
Appendix II
In vitro protective effects of quercetin in MCF-7
cells despite an underlying toxicity profile
Blessing E. Obinaju and Francis L. Martin
Abstract: 35th Annual Meeting of the United Kingdom
Environmental Mutagen Society.
Mutagenesis 27 (2012) 789-816
178
Appendix III
Attenuated total reflection Fourier-transform infrared
spectroscopy detects real-time polyaromatic
hydrocarbon toxicity in fish tissues.
Blessing E. Obinaju and Francis L. Martin
Abstract: 36th Annual Meeting of the United Kingdom
Environmental Mutagen Society.
Mutagenesis 29 (2014) 79-96
182
Appendix IV
Alterations in infrared spectral signature of
Heterobrachus bidorsalis reflects polyaromatic
hydrocarbon concentrations in Ovia river, Nigeria.
Blessing E. Obinaju and Francis L. Martin
Abstract: 43rd Annual Meeting of the European
Environmental Mutagen Society.
Mutagenesis 29 (2014) 497-559
185
Bibliography
188
VII
List of abbreviations
1-OHP: 1-hydroxypyrene
8-oxoGua: 8-oxo-7, 8-dihydroguanine
8-oxoGuo: 8-oxo-7, 8-dihydro-2´ deoxyguanosine
As: Arsenic
ATR: Attenuated total reflectance
B[a]P: Benzo[a]pyrene
Cd: Cadmium
Cu: Copper
Cr: Chromium
DDT: 1,1-dichloro-2,2-bis[4-chlorophenyl]-ethane,
DNA: Deoxyribonucleic acid
EC: Emerging contaminant
EMSC: Extended Multiplicative Signal Correction
FPA: Focal plane array
FTIR: Fourier transform infrared
Ge: Germanium
HCB: Hexachlorobenzene
HCH: Hexachlorocyclohexane,
VIII
Hg: Mercury
IR: Infrared
IRE: Internal reflection element
LC50: The concentration of a substance lethal to 50% of the organisms in a toxicity
test.
LD50: The individual dose required to kill 50 percent of a population of test animals
LDA: Linear Discriminant analysis
Low-E: Low-emissivity
MCF-7: Human mammary carcinoma cell line
Ni: Nickel
PAH: Polycyclic aromatic hydrocarbon
PC: principal components
PCA: Principal component analysis
PCB: Polychlorinated biphenyl
PCDD/Fs: Polychlorinated dibenzo-p-dioxins and furans
PLS: Partial least squares
PLSR: Partial least squares regression
PM: Particulate matter
RNA: Ribonucleic acid
IX
ROS: Reactive Oxygen species
Se: Selenium
SMR: Standard metabolic rate
SNR: Signal to noise ratio
SOCs: semi-volatile contaminants
SRs: Steroid receptors
UV: Ultra violet
ZnSe: Zinc Selenide
1
Chapter 1. General Introduction
1.1 Introduction .......................................................................................................................... 3
1.2 Environmental pollution and contamination of ecosystems ................................................ 4
1.2.1 Aquatic ecosystems ....................................................................................................... 5
1.2.2 Atmospheric ecosystems ............................................................................................... 6
1.2.3 Terrestrial ecosystems ................................................................................................... 7
1.3 Environmental pollution in Africa ....................................................................................... 8
1.4 Polycyclic aromatic hydrocarbons ....................................................................................... 8
1.4.1 Sources and composition ............................................................................................... 8
1.4.2 Environmental and biological fate ................................................................................ 9
1.4.3 Exposure, Biomarkers and Toxicity ............................................................................ 12
1.5 Vibrational Spectroscopy ................................................................................................... 21
1.5.1 Fourier Transform Infrared (FTIR) Spectroscopy ...................................................... 22
1.5.1.1 Background ........................................................................................................... 22
1.5.1.2 Instrumentation ..................................................................................................... 24
1.6 Data Handling .................................................................................................................... 28
1.6.1 Spectra Pre-processing ................................................................................................ 28
1.6.2 Feature Extraction, Construction and Selection .......................................................... 32
1.7 Computational Analysis ..................................................................................................... 32
1.7.1 Principal Component Analysis .................................................................................... 33
1.7.2 Linear Discriminant Analysis ...................................................................................... 34
2
1.7.3 Partial Least Squares Regression ................................................................................ 34
1.7.4 Combined Multivariate Analysis: PCA-LDA ............................................................. 35
1.7.5 Visualization of Processed Data .................................................................................. 35
1.8 Discussion .......................................................................................................................... 40
1.8.1 Potential applications of spectroscopy techniques in environmental
biomonitoring ....................................................................................................................... 40
1.8.2 Polycyclic aromatic hydrocarbon induced alterations in cultured cell
populations ........................................................................................................................... 40
1.8.3 PAH biomonitoring in Niger Delta, Nigeria ............................................................... 41
1.8.3.1 Understanding real-time PAH-induced toxicity in sentinel species ..................... 41
1.9 Aims and objectives ........................................................................................................... 42
References ................................................................................................................................ 44
3
1.1 Introduction
Environmental contaminants remain a high-risk hazard to human and wildlife
populations. Estuaries and other coastal waters within world regions receive a large
amount of synthetic chemicals in commercial use. These chemicals enter the various
water bodies via a variety of sources i.e. spills, dumping operations, urban runoffs,
municipal and industrial waste discharges. These sources contribute materials such as
petroleum hydrocarbons, polychlorinated biphenyls (PCBs), other such compounds,
pesticides and metals to the water bodies (Malins, 1980; Malins et al., 1984). Most
contaminants released into the environment are assimilated by biota with a variety
inducing adverse effects at high concentrations at the target organs.
Biomonitoring has facilitated the evaluation/risk assessment of several
chemical contaminants within the environment using biological organisms (and their
biodiversity) as indicators of environmental health. This is particularly useful because
these organisms when studied are able to reveal evidence of toxicity ranging from
cellular alterations to endocrine disruption as a result of exposure to chemical
contaminants (LeBlanc and Bain, 1997; Paoletti, 1999). Similarly, toxicological
studies have aided the identification and characterization of the various contaminants
based on the chemical dose (LD50, LC50) and biological response (carcinogens,
mutagens and genotoxins). These classifications are largely based on in vitro studies
which however accurate, are more often performed using high concentrations which
neither reflect concentration levels within the environment, nor the response of
biological systems to lower or multiple environmental exposures (Martin, 2007).
4
Biospectroscopy is a sensitive technique which generates a signature
(fingerprint) spectrum of cellular constituents present within cells or tissues, using the
ability of molecular bonds within each constituent to absorb and vibrate at specific
frequencies of the electromagnetic spectrum (Martin et al., 2010). Spectroscopic
methods optimised for use with most biological assays have provided more sensitive,
less expensive and less time consuming approaches with the possibilities of
monitoring cellular alterations as well as tracking these changes over time (Trevisan et
al., 2010).
Environmental contamination is a growing concern for most African and
developing countries. Within the Niger Delta region of Nigeria, the various activities
possibly responsible for the anthropogenic materials deposited within water bodies are
quite prevalent. This suggests that human and wildlife populations are exposed,
perhaps to varying concentrations of various environmental contaminants. Impacts
and consequences on the resident populations remain largely unknown due to a
scarcity of studies providing region-specific data (Essien et al., 2011).
Using vibrational spectroscopy, this thesis identifies polycyclic aromatic
hydrocarbon (PAH) induced alterations in sentinels Heterobranchus bidorsalis and
Ipomoea aquatic obtained from the Niger Delta region of Nigeria.
1.2 Environmental pollution and contamination of ecosystems
Environmental awareness has increased global concerns regarding the effects of
pollution and contaminants. Studies are increasingly designed to assess/understand the
ecological effects of contaminants i.e. direct effects on organisms, populations and
communities including the ecological state of endangered species and their habitats
(Chapman, 2004). Declining biodiversity populations and the link to environmental
5
population is equally an important concern. However, declines in biodiversity
population could be difficult to detect as various species occupy same habitats and are
thus equally vulnerable to the various forms of environmental alterations such as
habitat degradation, deforestation, draining of wetland and xenobiotic contamination
(Gibbons et al., 2000). Furthermore, the contamination of one component often leads
to contamination of one or all other components of the global ecosystem.
1.2.1 Aquatic ecosystems
The aquatic environment provides a sink for several contaminants with varying toxic
potentials (Kelly and Giulio, 2000). Contamination of most aquatic bodies occurs as a
result of the contamination of other components (air/land) of the environment. Run-off
from contaminated land surfaces and atmospheric depositions are likely avenues by
which most aquatic environments receive chemical contaminants (Bayen, 2012).
Contaminants such as petroleum hydrocarbons, heavy metals, pesticides as well as
several other chemical compounds, can cause direct toxic effects when released into
aquatic environments and sensitive species may be impaired by sub-lethal effects or
decimated by lethality (Fleeger et al., 2003). Synthetic organic compounds mostly
produced for industrial, domestic, or agricultural use, unless specifically removed by
wastewater treatment processes, may persist as part of the effluent and be released into
receiving waters as trace pollutants. Receiving waters for trace pollutants may
constitute a direct source of drinking water or indirectly reach a water supply as
recharge water (Murray et al., 2010). Advanced technologies involving the use of
granular activated carbon, membrane technology, ozonation, and ultraviolet radiation
have been used with relative success to remove pharmaceutical and environmental
contaminants from water destined for human consumption (Dorne et al., 2007).
6
Synthetic compounds classed as environmental contaminants (Murray et al.,
2010) have been detected in aquatic ecosystems in various parts of the world (Antizar-
Ladislao, 2008; Oehme, 1991). In particular, pesticides (carbamates,
chloroacetanildes, chlorophenoxy acids, organochlorines, organophosphates,
pyrethroids, and triazines) mainly organochlorines and organophosphates have the
potential to induce adverse effects in biota and humans (Leong et al., 2007; Murray et
al., 2010). A wide range of pesticides including HCH: hexachlorocyclohexane, DDT:
1,1-dichloro-2,2-bis[4-chlorophenyl]-ethane, HCB: hexachlorobenzene and lindane,
have been detected in precipitation, fresh and marine water within Europe (Dubus et
al., 2000; Graymore et al., 2001; Loos et al., 2009). Most environmental contaminants
are detected in low concentrations, normally in nanogram (ng) or microgram per litre
(µgL-1) range and are referred to as emerging contaminants (ECs) due to their
potential to adversely alter human/environmental health (Murray et al., 2010).
1.2.2 Atmospheric ecosystems
Industrialization in the various regions of the world has been greatly associated with
the emission of various substances which constitute atmospheric contaminants and
increase air pollution. Depending on emission sources, these contaminants may
contain complex mixtures of chemical and/or biological components, including viable
or non-viable microorganisms and fragments of microorganisms which could include
toxic components such as endotoxin and mycotoxins (Gangamma, 2012).
Atmospheric contaminants i.e. particulate matter (PM) and organochlorines have
considerable potential to persist in the atmosphere and be transported over long
distances. Long range transport is a term widely used to describe the transport of
contaminants over a few to many thousands of kilometres and as such, long range
atmospheric transport contributes to the dispersion of contaminants over several
7
distances, along with other forms of long-range transport via sea currents, biota and
ice transport in polar regions (Dubus et al., 2000). Other expressions such as ‘regional
scale’ and ‘meso-scale’ have also been used to describe the movement of
contaminants (Glotfelty et al., 1990).
Atmospheric contaminants are widely associated with several forms of adverse
health conditions in humans (Bell and Holloway, 2007). PM fractions of air pollution
increase reactive oxygen species (ROS) generation (Knaapen et al., 2004) and other
indirect effects mediated by pulmonary oxidative stress and inflammatory responses
(Brook et al., 2004).
1.2.3 Terrestrial ecosystems
Within terrestrial ecosystems, pasture vegetation and soil/sediments are the main
concentrated sources of most contaminants. Atmospherically deposited semi-volatile
contaminants (SOCs) such as polycyclic aromatic hydrocarbons (PAHs), PCBs,
polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs) are primarily intercepted by
pasture vegetation and the contaminated vegetation becomes the entry point of these
chemicals into the agricultural food chain (Smith et al., 2006). Other contaminants
include elemental compounds such as copper (Cu), nickel (Ni), arsenic (As), mercury
(Hg), selenium (Se), cadmium (Cd) and chromium (Cr). These compounds as well as
other such trace and heavy metals arise from anthropogenic activities such as mining,
smelting of metals, manufacturing operations and the use of soil fertilizers in
agriculture (Llabjani et al., 2014). Exposure to trace and heavy metal contamination
has been shown to be endocrine disrupting and capable of altering gene regulation via
the closely related glucocorticoid, mineralocorticoid, progesterone, and androgen
steroid receptors (SRs) at concentrations as low as 0.01μM (Davey et al., 2008).
Exposures have also been shown to significantly decrease acetylcholinesterase
8
activity in zebrafish (Danio rerio, Cyprinidae) (Richetti et al., 2011), increase ROS
generation in various systems (Jomova and Valko, 2011; Pinto et al., 2003) and alter
standard metabolic rates (SMR) (Rowe et al., 2001).
1.3 Environmental pollution in Africa
Environmental pollution in Africa and developing countries remains a global concern,
particularly due to a scarcity of robust studies providing data regarding exposure
levels and risk estimates for such regions and its resident population (Bruce et al.,
2000), compared to Europe and other developed countries (Beelen et al., 2014; Brauer
et al., 2008; Brunekreef and Holgate, 2002; Dimakopoulou et al., 2014; Dockery and
Pope, 1994; Dockery et al., 1993; Fleisch et al., 2014; Schwartz, 1994). Although
many studies document evidence of environmental contamination within various
regions of Africa (Akanni, 2010; Doherty et al., 2010; Essien et al., 2012; Essien et
al., 2008; Gwaski et al., 2013; Manirakiza et al., 2002), very few epidemiology or
biomonitoring studies have been conducted to assess the risks associated with
exposures, and most of these studies are focused on the contamination levels in soil
and sediments as opposed to water which constitutes a ready exposure route to human
and animal species.
1.4 Polycyclic aromatic hydrocarbons
1.4.1 Sources and composition
PAHs are semi-volatile organic chemicals and tend to persist in the environment
(Moeckel et al., 2013). PAHs are ubiquitous environmental pollutants which occur
more commonly as complex mixtures and derived from three sources: fossil fuel
(petrogenic PAH), combustion of organic matter (pyrogenic PAH) and the
9
transformation of natural organic precursors in the environment by relatively rapid
chemical/biological processes (biogenic PAH) (Lima et al., 2007; Neff et al., 2005).
Commonly measured PAH compounds contain two or more fused benzene (aromatic)
rings depending on their sources and represent possibly the largest class of
environmental carcinogens (Van Metre et al., 2000). PAH input into the various
ecosystems originate from a variety of natural (volcanic eruptions, oil seeps and forest
fires) and anthropogenic (vehicular emissions, fossil fuel and wood combustion)
sources. They are of toxicological importance due to their mutagenicity and
carcinogenic potential (Fent and Bätscher, 2000; Malins et al., 1997).
1.4.2 Environmental and biological fate
PAH deposition in various ecosystems, their sorption to aerosol organic matter and
atmospheric long range transport influenced by gas-particle partitioning, are selected
reasons for their persistence within the environment (Ma et al., 2013). PAHs emitted
into the atmosphere in exhaust gases or by volatization are usually transported over
long distances in association with soot particles or PM (Jager et al., 2000; Moeckel et
al., 2013; Ruchirawat et al., 2002). PAHs are particularly persistent (under anaerobic
conditions) within terrestrial ecosystems e.g. soil, and constitute a potential threat to
soil organisms (Jager et al., 2000).
Within contaminated ecosystems, PAHs as nonpolar organic chemicals have
low aqueous solubility and high affinity for adsorption to sediments, organic particles
as well as absorption by biological organisms (Neff et al., 2005). Organisms may
uptake PAHs via dermal or dietary routes of exposure (Fig. 1) and these chemicals
tend to accumulate in fatty tissues of aquatic/terrestrial organisms (Mackay et al.,
2006; Watanabe et al., 2005). PAHs can be bioconcentrated or bioaccumulated in
10
lower trophic levels but are rapidly metabolised by organisms e.g. vertebrates, in
higher levels.
PAHs are deposited in aquatic ecosystems via a number of sources including
spills, urban runoff as well as municipal and industrial waste discharge (Lima et al.,
2007; Malins et al., 1984). Generally, contaminant concentrations including PAHs in
aquatic organisms vary over time and space because of the influences of natural
processes and human activities. For example, within one species such as lake trout,
contaminants that bioaccumulate and biomagnify (e.g., methyl Hg and persistent
organic pollutants [POPs]) can be 5- to 10-fold higher in one lake than in another
neighbouring system because of inherent differences in the species' ecology, the
systems' characteristics, or the activities occurring in the watershed (Clements et al.,
2012). PAHs released into aquatic systems rapidly become associated with sediments
where they may become buried and persist until degraded, resuspended,
bioaccumulated or removed by dredging (Cerniglia, 1993). The possible fates of
PAHs in the environment include volatization, photooxidation, chemical oxidation,
bioaccumulation, adsorption to soil particles, leaching and microbial degradation
(Haritash and Kaushik, 2009; Wild and Jones, 1995).
Biodegradation of PAHs has been observed in soils and aquifers (Johnsen and
Karlson, 2007), and has been demonstrated to occur under both oxic and anoxic
conditions (Bamforth and Singleton, 2005). Studies (Meckenstock et al., 2000; Zhang
et al., 2000) have proposed a mechanism for the anaerobic degradation of PAHs
(naphthalene) which includes the carboxylation of the aromatic ring to 2-naphthoic
acid, activating the aromatic ring prior to hydrolysis. Stepwise reduction of 2-
naphthoic acid via a series of hydrogenation reactions results in decaclin-2-carboxylic
acid which is subsequently converted to decahydro-2-naphthoic acid. Others include
11
the bacterial and fungal metabolism of PAHs (Bamforth and Singleton, 2005) . There
is documentation of a large diversity of bacteria species that are able to oxidise PAHs
using dioxygenase enzymes, including organisms from the genus Pseudomonas and
Rhodococcus (Cerniglia, 1992; J. G. Mueller et al., 1996). Overall, the low molecular
weight PAHs are more volatile, water soluble and less lipophilic than their high
molecular weight relatives (Wild and Jones, 1995).
Fig. 1: Hypothetical aquatic food web that could occur in contaminated soil/water
bodies. Arrows show how nutrient and energy (including contaminants) are
transferred from one organism to another in feeding relationships. A more complex
food web would include more organisms than shown e.g. decomposers such as
bacteria which break down dead organic matter and recycle nutrients within the
ecosystem.
12
1.4.3 Bioavailability versus Bioaccessibility
Bioavailability and bioaccessibility are complex issues that determine whether or not
adverse effects are to be expected when organisms or plants are exposed to
contaminants. Therefore, the determinants of- and the definitions of bioavailability
and bioaccessibility must be understood if one is to monitor or, ultimately predict the
effects of potential environmental mutagens (Peijnenburg and Jager, 2003).
Bioavailability is a widely used term and has its origins in pharmacology where it is
defined as the fraction of an administered dose of unchanged drug (i.e., parent
compound) that reaches the systemic circulation and is one of the principal
pharmacokinetic properties of drugs. In ecotoxicology, bioavailability has been
defined as the amount of chemical that is actually taken up from the environment and
is available to cause a biological response where uptake may include binding to or
diffusion through cell membranes including bioaccumulation (McLaughlin and
Lanno, 2014).
Within ecotoxicology particularly when applied to contaminants in soil and aquatic
systems, it is reckoned that the term bioavailability is used inconsistently and
imprecisely (Semple et al., 2004). Thus, Semple et. al., (2004) proposed the following
definition- “the concentration of the compound which is freely available to cross an
organism’s cellular membrane from the organism’s environment at a given time”.
McLaughlin and Lanno, (2014) reckons the fundamental requirement in the definition
of bioavailability is that it should be measured in a biological receptor and not simply
by chemical or physical analysis of the media, although specific measures can be
made which can be correlated to the biological fraction.
13
Bioaccessibility is a more narrowly used term having its origins in soil science (but
rarely used in aquatic toxicology), and is a surrogate measure of bioavailability
(McLaughlin and Lanno, 2014). Again, Semple et. al., (2004) define bioaccessibility
as “the concentration of the compound which is available to cross an organism’s
cellular membrane from the environment, if the organism has access to the
compound”. This definition incorporates a time and space dimension where chemicals
that are spatially or temporally unavailable to the organism are bioaccessible, but not
bioavailable. Thus according to this definition, the magnitude of the bioaccessible
pool of the chemical is never less than the magnitude of the bioavailable pool and
could also be termed the “potentially bioavailable” pool given no spatial or temporal
constraints.
Monitoring bioavailable and bioaccessible fractions in itself may not be considered an
endpoint of assessment; rather, the focus is often on assessing adverse risks at species,
community, population, or ecosystem levels where bioavailable and bioaccessible
fractions can be related to adverse effects in organisms on the basis of the critical
body burden concept. The critical internal body burden being the threshold
concentration in the body above which physiological functions are irreversibly
impaired (Peijnenburg and Jager, 2003). However, properly distinguishing both terms
is highly important as it forces practitioners to consider what they actually measure
using biological and chemical assays (Semple et al., 2004).
14
1.4.4 Exposure, Biomarkers and Toxicity
Human and wildlife exposure to PAHs is unavoidable since most PAH compounds
occur ubiquitously in the environment and at various workplaces. The earliest
examples of occupational cancer among chimney sweeps, workers exposed to coal-tar
products, workers in iron foundries, coke ovens and aluminium production plants, is
generally agreed to be the result of exposure to PAHs (Phillips, 1999). However, for
non-occupationally exposed individuals, diet, ambient air, tobacco smoke and coal-
tar-containing medications are the main sources of PAH exposure (Scherer et al.,
2000). Mainstream smoke yields of B[a]P for filter cigarettes amount to about 10
ng/cigarette, leading to an intake of about 200 ng/day for a pack-a-day cigarette
smoker (Grimmer et al., 1987; Hoffmann, 1997). In the diet, the estimated daily intake
of B[a]P ranges from 120-2800 ng/day, with intake from ambient air by inhalation and
from water predicted to contribute about 2% and 1% respectively, to the total daily
intake in non-smokers (Hattemer-Frey and Travis, 1991).
Biomarkers are physiological or biological responses including variation in
cellular or biochemical components, processes, structure or functions measurable in
biological systems and/or samples as response to contaminant exposure (Obinaju and
Martin, 2013). Biomarkers are useful tools for assessing exposure and induced toxic
effects in human and wildlife populations (Table 1). They constitute a biological
response that is dose-dependent with toxicant exposure and can be used to monitor
exposure and/or effects, with the possibility to intrinsically link toxic compounds to
the mechanism by which they cause effects. These responses are most often observed
in biological organisms, including their biodiversity (LeBlanc and Bain, 1997;
Valavanidis et al., 2006). Biomarkers indicating exposure to PAHs especially in
humans include 8-oxo-7,8-dihydroguanine (8-oxoGua), 8-oxo-7,8-dihydro-2'-
15
deoxyguanosine (8-oxodGuo), 1-hydroxypyrene (1-OHP) and 1-OHP-glucuronide
(Gedik et al., 2002).
On the other hand, biomonitoring involves the use of organisms often regarded
as sentinels or biomonitors. These organisms (including the various life stages) are
often sensitive to one or more potential toxic compounds within the environment.
Thus, a biomonitor could be described as organisms within a test system which
provide quantitative information on the quality of its immediate environment
(Madejón et al., 2006; Markert et al., 2003). This information is often presented in any
of the following ways: physiological, chemical or behavioural modifications.
16
Table 1: Biomarkers indicating exposure and toxic responses in various organisms to
chemicals, including polycyclic aromatic hydrocarbons.
* biomarkers of exposure to specific classes of chemicals particularly heavy
metals such as lead, cadmium, etc. (Brambilla et al., 1986; Gedik et al., 2002; Park
et al., 2006; Scherer et al., 2000; Tintos et al., 2007).
Organism Biomarkers
Humans 8-Oxo-7,8-dihydroguanine (8-oxoGua), 8-oxo-7,8-dihydro-2'-deoxyguanosine
(8-oxodGuo), Catalase activity, glutathione S-transferase (GST), 1-
hydroxypyrene (1-OHP) 1-OHP-glucuronide, Monohydroxy-phenanthrenes,
Malonaldehyde (MDA), DNA damage/micronucleus formation, 4-
Hydroxyalkenals
Molluscs Lysosome membrane stability, Lysosomal lipofuscin, Lysosomal neutral lipid,
CaATPase activity, Catalase activity, Total oxidant scavenging capacity,
Acetylcholinesterase activity, Malondialdehyde, DNA damage/micronucleus
formation, Glutathione-S-transferase in haemolymph, *Metallothioneins
Amphibians Lysosome membrane stability, Lysosomal lipofuscin, Glutathione-S-
transferase, Glutathione peroxidase, Glutathione reductase, DNA
damage/micronuclei, Gonad morphology/atrophy, CYP1A [Ethoxyresorufin-
O-deethylase (EROD)], Vitellogenin steroid hormones, Acetylcholinesterase
activity, *Metallothioneins
Birds Ferritin and Haptoglobin,
Lichens Photosynthesis, Chlorophyll content/degradation, Endogenous auxin levels,
Ethylene production
17
Fig. 2 Processes affecting organism uptake of external substances via biological
membranes, their internal distribution, and possible effects. Modifying factors often
include genetic variability, age, gender and lifestyle of the exposed organism.
18
Toxicity is controlled by toxicokinetics that govern the bioaccumulation and
distribution of chemicals in tissues (based on their physical and chemical properties
and facility for biotransformation) and by toxicodynamics, which govern the
biochemical and physiological response of the organism (Fig. 2). The closer the
relationship between the concentration of the toxicant in whole tissues and the
concentration at the site of toxic action, the better the interpretation of the dose-
response relationship (McCarty et al., 2011). Exposure concentration and composition
of compounds is often modified by biotransformation of the compound within the
exposed organism, increasing the parent compound elimination rate and decreasing
the equilibrium concentration of the parent chemicals in tissues. However,
biotransformation can change the inherent toxicity of the parent compound because
metabolites can be more or less toxic.
Enzyme systems (mixed function oxidases e.g. cytochrome p450s) exists in
biological organisms which promote the conversion and biotransformation of
hydrocarbons to metabolites (Fig. 3) capable of altering biological macromolecules
e.g. DNA (Boysen and Hecht, 2003; Malins and Hodgins, 1981). The toxicity of
PAHs occurs as a result of this biotransformation. In themselves, PAH parent
compounds are rather unreactive and express little toxicity (Walker, 2012). Major
research on toxicity particularly human toxicology focuses on the mutagenic and
carcinogenic actions of PAHs because, DNA adducts formed by metabolites of
carcinogenic PAHs and other compounds predispose organisms to mutation cancers
(Hemminki et al., 2000). While acute toxicity of PAHs to mammals is low, the
toxicity of PAHs to aquatic organisms may arise from bioactivation, as well as depend
on the level of ultraviolet (UV) radiation to which the organism or test system is
exposed (Lampi et al., 2006). PAHs possess the ability to undergo photo-oxidation
19
(McConkey et al., 2002) and have been shown as photo-mutagenic with a possibility
of being activated by light irradiation, without requiring metabolizing enzymes (Yan
et al., 2004). The vast majority of toxicity studies are carried out using in vitro assays
including Bacterial Mutagenicity Assays (Gatehouse, 2012) and the Comet assay
(single-cell gel electrophoresis) (Speit and Hartmann, 2006). The various assays used
for toxicity testing are quite sensitive and detect various potential toxic compounds.
However, they are also prone to false positives and present the challenge of
extrapolating results to realistic and environmentally relevant scenarios.
20
Fig. 3: The metabolic activation of a typical polycyclic aromatic hydrocarbon
(Benzo[a]pyrene) into DNA reactive metabolites. Following exposure, most chemical
carcinogens are subject to biotransformation, catalysed by “xenobiotic metabolizing
enzymes” such as cytochrome P450-dependent monooxygenases (CYPs), hydrolases,
and transferases. This process could lead to the generation of electrophilic derivatives
which are capable of reacting with DNA and inducing mutations of tumour
susceptibility genes such as oncogenes.
21
1.5 Vibrational Spectroscopy
Molecular vibrations can range from the simple coupled motion of the two atoms of a
diatomic molecule to the much more complex motion of each atom in a large poly-
functional molecule. Molecules with N atoms will have 3N degrees of freedom, three
of which represent translational motion in mutually perpendicular directions (x, y and
z axes) and three represent rotational motion about the x, y and z axes. The remaining
3N – 6 degrees of freedom give the number of ways that the atoms in a nonlinear
molecule can vibrate (Griffiths and De Haseth, 2007)
A complex molecule exhibits a variety of vibrational modes which involve the
whole molecule. However, some of these molecular vibrations are associated with the
vibrations of individual bonds or functional groups (localized vibrations). These
localized vibrations are stretching, bending, twisting, rocking or wagging (Williams et
al., 1995). The study of the interaction between matter and electromagnetic radiation
depends on the wavelength or frequency of the radiation, such that regions of the
electromagnetic spectrum become associated with various types of spectroscopy, and
the frequency ranges named after the most common source of the radiation (e.g. X-
rays) or its practical use (e.g. Radio). These modes are dependent on the type of atoms
present and their structural arrangements (Painter et al., 1982).
Vibrational spectroscopic techniques have become potential tools for non-
invasive optical tissue diagnosis and have been applied to study a wide variety of
pathologic states. A wide variety of biological tissues have been studied using various
forms of vibrational spectroscopy especially infrared (IR) (Movasaghi et al., 2008)
and Raman (Movasaghi et al., 2007) spectroscopy.
22
1.5.1 Fourier Transform Infrared (FTIR) Spectroscopy
1.5.1.1 Background
IR rays were discovered by William Herschel in 1800 and are absorbed by matter in
the form of several bands localized in discrete frequency intervals. The basis for the
widespread use of IR spectroscopy is the observation that many chemical groups such
as C = O, absorb in a relatively narrow frequency range, irrespective of the nature of
the other functional groups present. Within this frequency range, the observed
frequency can be correlated to specific chemical structures and the spectral pattern
may be likened to a “molecular fingerprint” particularly because similar molecules
may have significantly different IR spectra, especially in the region below 1500 cm-1
(Painter et al., 1982). The IR spectra results from transitions between quantized
vibrational energy states with the usual range between 4000 cm-1 at the high frequency
range and 625 cm-1 at the low frequency end (Griffiths and De Haseth, 2007).
In the initial stages, spectra acquisition was a time consuming process because
spectrometers utilized the technology of a moving grating monochromator, to disperse
the single wavelength of the spectrum from a broad range of wavelengths and
therefore, only a wavelength of single resolution could be detected at a given time
(Stuart, 2005). With the introduction of interferometers, light covering the whole
frequency range, typically 5000 – 400 cm-1, is split into two beams and either one
beam is passed through the sample or both beams are passed, but one beam is made to
traverse a much longer path than the other. The recombination of the two beams
produces an interference pattern that is the sum of all the interference patterns created
by each wavelength in the beam. By systematically changing the difference in the two
paths, the interference patterns change to produce a detected signal varying with
optical path difference. This pattern is the interferogram and although it looks nothing
23
like a spectrum, Fourier transform of the interferogram using a computer built into the
machine, converts it into a plot of absorption against wavenumber which resembles
the usual spectrum obtains by the traditional method (Williams et al., 1995). FTIR
method of acquiring spectra is faster and provides a higher signal to noise ratio (SNR).
Fig 3: showing a schematic illustration of the internal components of a Fourier
transform infrared spectrometer (FTIR), fitted with a Michelson interferometer.
24
Common IR light sources are globar (black body) or synchrotron-based
radiation, where globar is a silicon carbide thermal mid-IR source, emitting radiation
from λ = 2.5 µm - 25µm (ῡ = 4000 – 400 cm-1), and synchrotron-based radiation is
~1000 times brighter than globar sources, producing IR spectra with a significantly
higher SNR (Kelly et al., 2011).
The potential of a stand-alone FTIR spectrometer may seem great. However,
the coupling of an FTIR to a visible light microscope (FTIR microspectrometry)
greatly increases its potential as it permits the examination of complex molecules (e.g.
biological tissues) and heterogeneous samples. Infrared microscopes are often high
quality visible microscopes redesigned for use with IR radiation. Detection by
microscopy may be accomplished by laser-scanning a point illuminated on the sample
or by using wide-field illumination and focal plane array (FPA) or linear array
detectors (Baker et al., 2014)
1.5.1.2 Instrumentation
Transmission, transflection and attenuated total reflection (ATR) are the three major
IR-spectroscopic sampling modes by which spectra acquisition may be carried out. In
transmission mode, the IR beam is directed through a sample and collected by a
condenser whereas, in transflection mode, the beam is directed through the sample,
reflects off an IR-reflective surface [such as that found on low-emissivity (Low-E)
slides], travels back through the sample to the detector. With both measurements, the
sample thickness is an important criterion, as extremely thick samples will attenuate
the IR beam beyond the range where absorption is proportional to chemical
concentration and very thin samples will result in low absorption where acquired
spectra signal is flooded with noise (Kelly et al., 2011)
25
The ATR has grown into the most widely practiced technique in IR
spectrometry especially because, the technique involved requires little or no sample
preparation and consistent results can be obtained with relatively little care or
expertise. The ATR mode of spectra acquisition involves passing the IR beam through
an internal reflection element [(IRE) usually an IR-transparent element)] with a high
refractive index [e.g. Zinc Selenide (ZnSe), type II diamond or Germanium (Ge)]
(Walsh et al., 2007). When the IRE is placed in contact with the sample and the beam
passed through it, the beam is totally internally reflected, generating an evanescent
wave which penetrates a few µm beyond the element into the sample (Kelly et al.,
2011). The depth of penetration varies from a fraction of a wavelength up to several,
depending on the index of refraction of the element and the angle of the incident
radiation with respect to the interface between sample and element. It is also
wavelength-dependent, increasing with increasing wavelength and has the
consequence that if the sample selectively absorbs certain wavelength components of
the evanescent radiation, then attenuation of the reflected beam occurs preferentially
at the wavelength of absorbance bands (Walsh et al., 2007).
Attenuated total reflection can be said to be the most versatile of all IR
sampling techniques because, it requires very little sampling preparation and can be
used on samples of almost all morphologies, while often maintaining the structural
integrity of the sample. ATR is in large part a surface technique and the interrogation
of sample is largely limited to the depth of penetration of the measurement (Griffiths
and De Haseth, 2007).
26
Table 2: FTIR spectroscopy modes commonly used for the interrogation of cellular materials
Mode Suitable samples Substrate Typical
interrogation area
(m)
Pros Cons
ATR Tissues, cells,
biofluids
Calcium or
barium fluoride,
zinc selenide,
MIRR IR Low E
250 × 250 High SNR
Reduced scattering
Analysis of large target
area
Better for aqueous
samples
Low resolution
Can be destructive due to
pressure
Air between sample and
IRE may affect spectra
Minimum sample
thickness required
(around 2.3 m)
Transmission Tissues, individual
cells, cellular
components,
biofluids
Calcium or
barium fluoride,
zinc selenide
5× 5 to 150 × 150 High resolution
Non destructive
Automated stage allows
for spectral acquisition at
several different
locations of choice with
little user interaction
Lower SNR than ATR
Maximum sample
thickness required
Longer sample and
machine preparation
required
Transflection Tissues, individual
cells, cellular
components,
biofluids
Calcium or
barium fluoride,
zinc selenide
5× 5 to 150 × 150 High resolution
Non destructive
Automated stage allows
for spectral acquisition at
several different
locations of choice with
little user interaction
May give rise to standing
wave artifacts
Lower SNR than ATR
Maximum sample
thickness required
Longer sample and
machine preparation
required
Source: (Baker et al., 2014).
27
Fig. 4: Schematic illustration of spectra acquisition using attenuated total reflection
mode in infrared (IR) spectroscopy.
28
1.6 Data Handling
Many studies involve the processing of data derived from several samples grouped
into a number of classes and possible containing several variables. The interrogation
of samples using spectroscopy, generates large and complex datasets which require
robust methods to extract specific information of interest e.g., factors responsible for
variability between groups or classes in the dataset. Using specific approaches or a
combination of two or more approaches, it is possible to reduce the complexity of
datasets and extract meaningful underlying variance within variables.
1.6.1 Spectra Pre-processing
Spectra pre-processing, regarded also as the manipulation of spectra is often
performed using software packages built into an FTIR spectrometer (Table 3).
Following spectra acquisition and prior to analysis, the acquired spectra must be pre-
processed in order to account and correct for noise, sloping baseline effects,
differences in sample thickness or concentration, and to select the regions of interest.
This process can be summarised thus: cutting, baseline correction and normalization.
Skewed baselines in acquired spectra could occurs as a result of several factors
including resonant Mie scattering; occurring when the wavelength of IR light is
comparable or smaller than some of the molecular structures through which it passes,
causing the passing light to scatter. Other factors may be reflection, temperature,
concentration or instrument anomalies. Baseline correction can be achieved using
techniques such as the rubberband baseline correction; stretching the spectra down so
minimal areas of the spectral region of interest are used to fit a convex polygonal line
and then subtracted from the original spectrum. Baseline correction may also be
carried out by differentiating the spectra twice, causing the spectra to lose their slope.
29
(Kelly et al., 2011). Spectral distortions due to Mie scattering can be corrected using
the Extended Multiplicative Signal Correction (EMSC) algorithm (Kohler et al.,
2008), which is particularly effective where Mie scattering is weak and where the
spectra do not show strong distortion of the Amide I band (Bassan et al., 2010).
Normalization is employed to scale the spectra and remove spectral changes
accountable by the thickness or concentration of the sample, thus making all spectra in
a batch comparable to each other. Normalization approaches include the Min-max
normalization; applied when a known peak is stable and consistent across the
specimens e.g. Amide I in animal cell and tissue samples, and vector normalization;
where each spectrum is divided by its Euclidean norm rather than relying on a specific
peak. The vector normalization method is recommended where differentiation has
been carried out as a baseline correction method (Kelly et al., 2011). Pre-processing of
spectra is an important step in data handling particularly with IR spectroscopy. the
outcome of computational analysis is heavily dependent on the effectiveness of the
techniques used to deal with unwanted variability (“noise”) in the data (Martin et al.,
2010).
Other forms of pre-processing techniques include spectral subtraction; mostly
applied to obtain the spectrum of a component in a mixture, De-noising; used to
enhance the information content of a spectrum by removing the “noise” in the
spectrum, and deconvolution; mathematical enhancement of spectrum resolution and
particularly used to distinguish the positions of overlapping bands within a spectrum.
It is important to note that transmission and single beam spectra should not be
subtracted because their peak heights and areas are not linearly proportional to
concentration (Smith, 2011).
30
Fig. 5: An illustration of a spectra pre-processing technique (baseline correction).
Acquired spectra (black) was baseline corrected (blue) using the rubberband baseline
correction method.
25012502250325042500.0
0.1
0.2
0.3
0.4
0.5
Wavenumber cm-1
Ab
so
rb
an
ce (
a.u
.)
31
Table 3: Existing FTIR spectroscopy data analysis software.
Software Website Description License
Cytospec www.cytospec.com Software for
hyperspectral imaging
(IR, Raman)
Commercial;
free demo
available
IRootLab irootlab.googlecode.com MATLAB toolbox for
biospectroscopy data
analysis
Open-source
OPUS www.bruker.com Spectral acquisition
software with data
processing capabilities
Commercial
Pirouette www.infometrix.com Chemometrics
modelling software
Commercial
Unscrambler X www.camo.com Multivariate data
analysis and design of
experiments
Commercial
PLS, MIA,
EMSC
toolboxes
www.eigenvector.com MATLAB toolboxes
for spectroscopy data
analysis
Commercial
OMNIC www.thermoscientific.com Spectral acquisition
software with data
processing capabilities
Commercial
PyChem http://pychem.sourceforge.net/ Package for univariate
and multivariate data
analysis
Open-source
ENVI, IDL www.exelisvis.com Integrated
development, data
analysis, image
processing suite
Commercial
Source: (Baker et al., 2014).
32
1.6.2 Feature Extraction, Construction and Selection
In statistics, “features” is a term synonymous with “input variables”, i.e., the inputs to
the subsequent analysis method. In IR spectroscopy, the wavenumber absorbance
intensities can be used as features. However, it is important to reduce the number of
variables within the dataset in order to avoid the “curse of dimensionality” which
manifests as over-fitting and can could lead to the poor performance of classifiers
when tested on independent data, or the formulation of incorrect hypotheses drawn
from exploratory analysis (Jain et al., 2000).
Feature construction and selection serves as an approach to reduce the number
of variables within a data set and include common techniques such as principal
component analysis (PCA), linear discriminant analysis (LDA) or partial least squares
(PLS), where the constructed variables are linear combinations of the wavenumbers
(Kelly et al., 2011). Although it is practically impossible to find the best subset from
all the existing 2n possibilities, where n is the number of wavenumbers originally
present in the dataset, several suboptimal feature selection strategies exist which could
rank the relevance of the wavenumbers individually based on an evaluation criterion
e.g. Pearson correlation or t-test, in order to determine which wavenumbers are
retained or, the application of a selection algorithm “wrapped around” a classifier used
to rank the subsets (Guyon and Elisseeff, 2003).
1.7 Computational Analysis
The complexity of the datasets generated from spectrometry presents the challenge of
extracting meaningful underlying variances within variables. Thus, computational
analysis employs mathematical algorithms (tools) to extract the variance within
variables. Most of the approaches are linear techniques of feature extraction and
33
multivariate data analysis tools such as PCA, LDA, PLS or a combination of PCA and
LDA (PCA-LDA) which generates loading vectors with the ability to identify the
contribution of each wavenumber-variable to generate the new variable (Trevisan et
al., 2012)
1.7.1 Principal Component Analysis
PCA is an unsupervised exploratory data analysis tool which reveals relationships in
data that might have otherwise been ignored or not observed. PCA is most often the
first analysis performed on a new dataset, because being unsupervised, it is unbiased
and reveals the most prominent variation patterns in data, whether these variations are
correlated to classes or not (Trevisan et al., 2012). Within IR spectroscopy, PCA is
employed to reduce dimensionality and generate a visualization of data. It is a linear
transformation of the wavenumber dataset operated by the PCA loadings matrix. The
loadings vectors (principal components [PCs]) within this matrix are eigenvectors of
the covariance matrix of the data and each loadings vector contains the coefficients of
a linear combination that generates one new variable called a PCA factor. PCA factors
are uncorrelated and each PC has a corresponding eigenvalue which exactly matches
the variance of its corresponding PCA factors, enabling these factors to be ranked
according to the magnitude of variance captured by each one. Thus, the first 3 PCs are
most commonly used as they contain the most variance, often up to 99%, ensuring
optimum visualization of the data (Kelly et al., 2011). Using PCA, each spectrum is
viewed as a single point or score in n-dimensional space and selected PCs are used as
Cartesian coordinates to reveal clusters which inform the formulation of hypotheses
regarding similarities or differences in dataset by exploiting proximity or segregation
levels between clusters. This approach allows the intrinsic dimensionality of large and
34
complex data to be interrogated and analysed for clustering when viewed in a
particular direction (Davies and Fearn, 2004).
1.7.2 Linear Discriminant Analysis
LDA is a supervised technique which is used to achieve class segregation. It is more
likely to over-fit, if the number of spectra is insufficient. Thus, it is generally
recommended that the number of spectra in the dataset be 5-10 times bigger than the
number of variable (Trevisan et al., 2012). LDA forms linear combinations of
variables dependent on the differences between the classes in the dataset and the LDA
loading vectors are successive orthogonal solutions to the problem to “maximize the
between-class variance over the within-class variance”. Following the application of
LDA on a dataset, the dataset will only have c – 1 variable, where c is the number of
data classes (Kelly et al., 2011). Applied to IR spectroscopy especially with regards to
answering biological questions, LDA is used to reduce confounding factors of within-
category heterogeneity whilst maximizing between-category discriminating
biomarkers (Martin et al., 2007).
1.7.3 Partial Least Squares Regression
PLSR is a supervised multivariate analysis method which addresses the problem of
making good predictions in multivariate datasets (Mehmood et al., 2012). PLSR
constructs a set of linear combinations of the wavenumbers the same way as PCA, but
uses the data classes in the construction. Where PCA ranks the PCs according to
variance within the dataset, PLSR employs a different approach by finding a sequence
of new variables that are maximally correlated with a numerical representation of the
data classes while being independent to each other. PLSR has a tendency to over-fit
and requires more validation than PCA (Kelly et al., 2011).
35
1.7.4 Combined Multivariate Analysis: PCA-LDA
The cascade application of LDA on the factors resulting from PCA is a popular
multivariate analysis performed on IR spectral datasets particularly with regards to
biomarker extraction (Trevisan et al., 2012). PCA-LDA gives loading vectors which
identify the contribution of each wavenumber-variable to generate the new variables
(factors). The weights for each factor are represented by a vector called a “loadings
vector”. PCA-LDA presents each scalar value of each factor as a “score” which may
be visualized through 1-, 2-, or 3-dimensional scatter plots also known as “scores
plots.” LDA allows for data visualization in the form of cluster vector plots which
may be used to identify biomarkers (i.e. wavenumbers) associated with specific
treatment conditions. Each cluster vector is a linear combination of the loadings
vectors and it can be plotted as y-values having the wavenumbers as x-values. It is also
possible to apply a peak detection algorithm to identify prominent peaks within each
cluster vector (Llabjani et al., 2011).
1.7.5 Visualization of Processed Data
Following the application of multivariate analysis, results could be visualized in
multiple fashions. Most commonly used are the scores and loadings plots as well as
the cluster vector approach. Scores plots are scatter charts drawn using the data values
obtained after multivariate analysis as Cartesian coordinates. Scores and loadings
plots provide a visual representation and interpretation of variables responsible for any
segregation, following the construction of factors from any one of the multivariate
techniques earlier discussed. The loadings vectors of the afore mentioned techniques
have the same resolution as the original spectra and their coefficients can be plotted
against the wavenumber axis to reveal the contributions of each wavenumber to form
each corresponding factor (Kelly et al., 2011).
36
The cluster vector approach is a geometric construction applied to linear
multivariate techniques although it is most commonly employed following the
application of PCA-LDA (German et al., 2006). The idea of cluster vectors follows
from the fact that loading vectors are found to be more informative when they “pass
through” data points rather than pointing towards void space. There is therefore one
cluster vector for each data class where each cluster vector is a vector that points from
the origin to the centre of its corresponding data class in the vector space spanned by
the vectors (Kelly et al., 2011).
37
Fig. 6 Flow diagram of data processing and analyses used in the various projects
contained in this thesis. Red boxes represent pre-processing methods
, Green box represents computational analysis using multivariate approach and the
purple boxes represent the output and visualization.
38
Fig. 7 An example of cluster vector derived following the application of multivariate
analysis. In this case, cluster vectors were used to visualize results after the
application of principal component analysis combined with linear discriminant
analysis (PCA-LDA). A peak detection algorithm was applied to detect prominent
peaks which distinguished each class in dataset (green and red lines) from the
reference class (black line).
9001100130015001700-0.07
-0.05
-0.03
-0.01
0.01
0.03
0.05
0.07
*
**
*
*
**
**
**
1740
1216 1214
961
957
1126
1123
1393
1516
1539
1616
*1620
Wavenumber cm-1
Co
eff
icie
nt
(a.u
.)
39
Fig. 8 showing the potential biomarkers extractable from the IR biochemical
fingerprint of cells and tissues. Alterations to any one of the identified regions of the
fingerprint could provide an insight as to the mechanistic action (e.g. genomic
damage) of the compound under observation.
40
1.8 Summary of publications
1.8.1 Novel biospectroscopy sensor technologies towards environmental
health monitoring in urban environments
Obinaju and Martin (2013) presents a brief introduction to the use of vibrational
spectroscopy techniques for real-time detection of sub-lethal effects of compounds in
the environment. It highlights previous successful applications of vibrational
techniques particularly in clinical diagnosis and most importantly, it presents a
hypothetical scenario using land snails (Helix aspersa) as potential biomonitors.
(Chapter 2).
1.8.2 Distinguishing nuclei-specific benzo[a]pyrene-induced effects in
MCF-7 cells from whole-cell alterations using Fourier-transform infrared
spectroscopy
Several chemical compounds possess the ability to alter metabolic processes within
various parts of the cell, alterations which are key to the onset or progression of
pathologic conditions. Obinaju et. al., (2015a) observed the changes occurring in the
nucleus the human mammary carcinoma (MCF-7) cell line as a result of exposure to
concentrations of a typical carcinogen polycyclic aromatic hydrocarbon:
benzo[a]pyrene. Observations were carried out using ATR-FTIR and the study
observed that ATR-FTIR was able to detect changes to nucleus and highlight
wavenumbers responsible for these changes. Importantly it was able to highlight
wavenumbers which could indicate the possibility of apoptotic induction at high
concentrations. (Chapter 3).
41
1.8.3 PAH biomonitoring in Niger Delta, Nigeria
1.8.3.1 Novel sensor technologies towards environmental health monitoring in
urban environments: a case study in the Niger Delta (Nigeria).
Environmental pollution in the Niger delta is a persistent concern. Using ATR-FTIR,
Obinaju et. al., (2014) interrogated various tissues of the African catfish
(Heterobranchus bidorsalis) and leaves of the water spinach (Ipomea aquatica)
sampled from the Ovia River; recipient of petroleum hydrocarbons. The study
observed that ATR-FTIR was able to discriminate between samples from various
sites. It was also able to discriminate between samples obtained at different seasons
and able to detect alterations in tissues relative to chosen controls (Chapter 4).
1.8.3.2 Linking biochemical perturbations in tissues of the African catfish to the
presence of polycyclic aromatic hydrocarbons in Ovia River, Niger Delta region.
Obinaju et. al., (2015b) measured the concentration of PAH compounds in the
dissolved phase of the Ovia River in the Niger Delta, and attempted to relate the
concentrations of PAHs detected to the observed biochemical changes in tissues of the
African catfish. It explored the potential impact of seasonal variations on the observed
changes. These changes were documented as shifts in centroid positions of absorption
bands as well as increased or reduced intensity to bands at certain wavenumbers
(Chapter 5).
42
1.8.3.3 Attenuated total reflection Fourier-transform infrared spectroscopy
reveals polycyclic aromatic hydrocarbon contamination despite relatively
pristine characteristics of site: results of a field study in the Niger Delta
Biomarkers for disease can be identified by comparing the IR spectra of malignant
tissue samples to the IR spectra signature of a reference ‘normal’ tissue. Similarly, it is
possible to distinguish between chemical exposures in tissues based on the IR spectra
of the exposed tissues. This study aimed to identify the biomarkers of PAH exposure
in the tissues of the African catfish by comparing tissues obtained from sites with
known contamination sources and potential contaminant compounds, to tissues
obtained from a relatively pristine site. It found that using spectra of samples exposed
to known compounds, ATR-FTIR was able to identify potential exposure to similar
compounds in samples from sites with undocumented contaminant history (Chapter
6).
1.9 Aims and objectives
This thesis is composed of four primary author research projects which investigate the
application of FTIR spectroscopy to detect and measure sub-lethal effects of potential
mutagens in the environment using sentinel organisms.
The study hypothesizes that 1) the technique ATR-FTIR is sensitive to and able to
detect minimal cellular changes occurring in tissues exposed to potential mutagens. 2)
ATR-FTIR can extract potential biomarkers to signature chemical induced changes in
tissues
Also included in the appendix is a co-author project which explores the potential
standardization of methods and procedures that could optimise the application of IR
43
spectroscopy to an even wider variety of biological questions including disease
screening and diagnosis.
To investigate the effects of low dose PAH exposure in intact cells and
isolated nuclei as a baseline for real-time environmental exposure scenarios,
using ATR-FTIR spectroscopy (Chapter 3).
To detect seasonal variations in exposure and real-time exposure effect in fish
tissue and plant leaves, using ATR-FTIR spectroscopy (Chapter 4).
To investigate the correlation of PAH concentrations detected in dissolved
phase of water column to the alterations in fish tissues (Chapter 5)
To explore the potential identification of biomarkers of PAH exposure in fish
tissues using ATR-FTIR spectroscopy (Chapter 6)
44
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54
Chapter 2
Novel biospectroscopy sensor technologies towards environmental health
monitoring in urban environments.
Blessing E. Obinaju and Francis L. Martin
Environmental Pollution 183 (2013) 46-53.
Contribution:
I prepared the first draft of the manuscript
……………………………… ………………………………
Blessing E. Obinaju Prof Francis L. Martin
55
Chapter 3
Distinguishing nuclei-specific benzo[a]pyrene-induced effects in MCF-7
cells from whole-cell alterations using Fourier-transform infrared
spectroscopy
Blessing E. Obinaju, Nigel J. Fullwood and Francis L. Martin
Toxicology 335 (2015) 27–34
Contribution:
I conducted all experiments for the study
Scanning Electron Microscopy was performed by Nigel Fullwood
I acquired the data and carried out the computational analysis
I prepared the first draft of the manuscript
……………………………… ………………………………
Blessing E. Obinaju Prof Francis L. Martin
66
Chapter 4
Novel sensor technologies towards environmental health monitoring in
urban environments: a case study in the Niger Delta (Nigeria).
Blessing E. Obinaju, Alozie Alaoma and Francis L. Martin
Environmental Pollution 192 (2014) 222-231.
Contribution:
I acquired the samples required for the project
I prepared processed and acquired 80% of the data and carried out
computational analysis.
I prepared the first draft of the manuscript
……………………………… ………………………………
Blessing E. Obinaju Prof Francis L. Martin
87
Chapter 5
Linking biochemical perturbations in tissues of the African catfish to the
presence of polycyclic aromatic hydrocarbons in Ovia River, Niger Delta
region.
Blessing E. Obinaju, Carola Graf, Crispin Halsall and Francis L. Martin
Environmental Pollution 201 (2015) 42-49
Contribution:
I acquired the samples required for the project
I prepared processed and acquired data for African catfish including carrying
out computational analysis.
Water analysis for polycyclic aromatic hydrocarbon was acquired by Carola
Graf, Crispin Halsall.
I prepared the first draft of the manuscript
……………………………… ………………………………
Blessing E. Obinaju Prof Francis L. Martin
103
Chapter 6
Attenuated total reflection Fourier-transform infrared spectroscopy
reveals polycyclic aromatic hydrocarbon contamination despite relatively
pristine characteristics of site: results of a field study in the Niger Delta
Blessing E. Obinaju and Francis L. Martin
Contribution:
I acquired the samples required for the project
I prepared processed and acquired data for African catfish and carried out
computational analysis.
I prepared the first draft of the manuscript
……………………………… ………………………………
Blessing E. Obinaju Prof Francis L. Martin
104
Attenuated total reflection Fourier-transform infrared spectroscopy reveals
polycyclic aromatic hydrocarbon contamination despite relatively pristine
characteristics of site: results of a field study in the Niger Delta
Blessing E. Obinaju and Francis L. Martin*
Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster LA1 4YQ,
UK
Corresponding author: Prof Francis L. Martin PhD, Centre for Biophotonics, LEC,
Lancaster University, Lancaster LA1 4YQ, UK; Tel.: +44(0)1524 510206; Email:
105
Abstract
Fourier-transform infrared (FTIR) spectroscopy is becoming a technique to detect
biochemical alterations in biological tissues, particularly changes due to sub-lethal
exposure to environmental contaminants. We have previously shown the potential of
attenuated total reflection FTIR (ATR-FTIR) spectroscopy to detect real-time
exposure to contaminants in sentinel organisms as well as the potential to relate
spectral alterations to the presence of specific environmental agents. In this study
based in the Niger Delta (Nigeria), changes occurring in fish tissues as a result of
polycyclic aromatic hydrocarbon (PAH) exposure at contaminated sites are compared
to the infrared (IR) spectra of the tissues obtained from a relatively pristine site.
Multivariate analysis revealed that PAH contamination could be occurring at a pristine
site, based on the IR spectra and significant (P <0.0001) differences between sites.
The study provides evidence of IR spectroscopy techniques’ sensitivity and supports
their potential application in environmental biomonitoring.
Keywords: African catfish; Environmental pollution in Nigeria; Fourier-transform
infrared spectroscopy; Heterobrachus bidorsalis; Niger Delta pollution; Polycyclic
aromatic hydrocarbon
106
1. Introduction
Human activities generate potentially toxic compounds, some with unusual
characteristics. Most of these compounds end up in various parts of the ecosystem and
constitute a degree of hazard to biological populations including humans. Synthetic
chemicals and materials such as petroleum hydrocarbons, persistent organic pollutants
(POPs), pesticides and metals which mostly contaminate aquatic systems, are more
often linked to advancements in industrialization (Li et al., 2001; Zhang et al., 2005).
Exposures to these compounds have been linked to a variety of adverse effects
including neurodevelopmental effects following in utero exposure (Perera and
Herbstman, 2011; Perera et al., 2006; Wormley et al., 2004).
Studies continue to show that chemical contaminants are capable of inducing
toxicity in organisms, even at very low concentrations (Kalantzi et al., 2004; Pang et
al., 2012; Ukpebor et al., 2011), and their accumulation in the tissues of organisms
following exposure, particularly aquatic and wildlife species, generates concern for
the possibility of contaminant transfer through the food chain (Gwaski et al., 2013;
Lozano et al., 2012). Several analytical techniques exist to biomonitor contaminants in
the ecosystem and within organisms. In recent years, the field of biospectroscopy, a
technique which employs the use of infrared (IR) spectrometry or the coupling of IR
spectrometry to other techniques (e.g., microscopy [IR microspectroscopy]) to
understand changes in cells and tissues, especially those which occur as a result of
exposure to environmental chemicals, has gained immense attention. The application
of biospectroscopy to observe these changes, is based on the knowledge of the
vibrational modes of biomolecules which generates spectral information [often known
as the “biochemical-cell fingerprint” (biofingerprint)] when exposed to IR radiation
(Martin et al., 2010). Based on changes to the biofingerprint, it is possible to
107
distinguish between cell/tissue types (German et al., 2006) with potential cell
characterization (Grude et al., 2007).
Biospectroscopy techniques are non-destructive to samples, relatively reagent-
free and can generate rapid, high-throughput and robust results in real-time with high
sensitivity to minimal changes within biomolecules (Martin et al., 2010). Thus,
biospectroscopy can be employed to study contaminant-induced responses in
organisms, using a wide variety of sample types and particularly, it has the potential to
biomonitor environmental contaminants in most sentinels, in real-time (Ibrahim et al.,
2012; Llabjani et al., 2012; Malins and Gunselman, 1994; Obinaju et al., 2014). These
techniques can be optimised for even more applications (Baker et al., 2014).
Biospectroscopy techniques involving the use of attenuated total reflection Fourier-
transform infrared (ATR-FTIR) spectroscopy require minimal sample preparation
(Martin et al., 2010; Obinaju and Martin, 2013) and have been shown to detect the
slightest chemical-induced variation in samples at very low (< 10-9 M) concentration
ranges (Ahmad et al., 2008; Llabjani et al., 2014; Llabjani et al., 2011; Ukpebor et al.,
2011).
We have previously shown that ATR-FTIR spectroscopy is able to
differentiate between real-time exposure effects both animal and plant tissues from
sites with varying degrees of environmental contamination (Obinaju et al., 2014).
Herein, we compare tissues of the African Catfish (Heterobrachus bidorsalis) from
sites with a known history of polycyclic aromatic hydrocarbon (PAH) contamination,
to samples from a relatively pristine site with no documented history of contamination
and no industrial activity. Our aim was to determine if we could signature PAH-
induced toxicity in fish tissues using ATR-FTIR spectroscopy.
108
2. Materials and Methods
Samples of Heterobrachus bidorsalis were collected in March 2013 by local
fishermen at Gelegele, Ikoro and Ifiayong in Edo and Akwa Ibom States, within the
Niger Delta region. Site descriptions as well as sample handling, tissue pre-processing
methods and spectral measurements have been previously detailed (Obinaju et al.,
2014). Briefly, each site was selected based on the documented knowledge of
industrial activities, which yield possible environmental contaminating compounds.
Gelegele and Ikoro are located in close proximity to petroleum exploration activities
and Ifiayong is a rural fishing community with no documented history of petroleum
exploration or similar industrial type activities. Each excised fish tissue was thinly
sliced (≤ 1-mm thick/slice) by hand using a Stadie-Riggs handheld microtome and
Thomas blade (Taylor et al., 2011). Each slice was rehydrated by washing twice in
dH2O. Sample slices were mounted on Low-E reflective glass slides (Kevley
Technologies, Chesterland, OH), allowed to air-dry and desiccated for a minimum of
24 h. prior to interrogation using ATR-FTIR spectroscopy.
2.1 Spectral acquisition and pre-processing
IR spectra were obtained using a Bruker Vector 27 FTIR spectrometer with Helios
ATR attachment containing a diamond crystal (Bruker Optics Ltd., Coventry, U.K.).
Data acquired for each experimental condition (i.e., each sample slide) consisted of 10
spectra, each from a random area of the tissue slice under interrogation, using an FTIR
imaging system coded for 32 scans per spectra and 3.84 cm-1 spectral resolution. The
ATR crystal was cleaned with dH2O, dried thoroughly and a new background
spectrum taken prior to analysis of a new sample. Raw spectra were acquired in the
4000 cm-1 - 400 cm-1 range. Spectra in the region of interest (1800 - 900 cm-1) were
selected and pre-processed (baseline corrected and normalized to Amide I peak) to
109
account and correct for noise, sloping baseline effects, differences in sample thickness
or concentration.
2.2 Computational analysis
Multivariate analysis [principal component analysis-linear discriminant analysis
(PCA-LDA)] were performed in MATLAB R2011b using an in-house developed
IRTools toolbox (Trevisan et al., 2013). Results were visualized either as scores plots
or cluster vectors plots, and the toolbox was set to identify the top six wavenumbers
responsible for site differences. The mean band/peak area of the absorbance at specific
regions was measured by calculating the integrated absorbance between the two
wavenumbers (max-min) of the given region.
2.3 Statistical analysis
Variation in the tissues within the dataset was tested for statistical significance using
Mann Whitney U-test, one-way analysis of variance (ANOVA) and Dunnett’s multiple
comparison tests, where the P-value of less than 0.05 (P <0.05) was considered
statistically significant.
110
3. Results
Figs. 1 and 5A show the mean spectral absorbance for the brain, kidney, heart, liver
and gill tissues of African catfish. Tissues showed relatively marked differences
within the lipid (~1740 cm-1), protein (~1700 cm-1 - 1400 cm-1) and DNA/RNA
(~1399 cm-1 - 900 cm-1) regions of the biofingerprint for all tissues, excluding gills
where very subtle alterations to the DNA/RNA region were observed (Fig 5). Of note,
most tissue (brain, liver, and gills) spectra sampled from Ifiayong seemed most
congruent with tissues sampled from Gelegele.
Using the first LDA factor (LD1) in a one-dimensional (1-D) scores plot, the
degree of variation in tissues between the sampling sites was visualized, following the
application of PCA-LDA (Figs. 2 and 5B). The tissues obtained from Ikoro seemed
most different from the corresponding tissues from other sites and produced a positive
index along the LD1 space in most tissues [brain, liver (Fig 2) and gill (Fig 5)]. The
variation between sites was tested using one-way analysis of variance and Dunnett’s
multiple comparison test, comparing each site against the chosen reference site
(Ifiayong). The variations between sites were significant with P <0.0001 in ANOVA
and P <0.01 in Dunnett’s multiple comparison test, for all tissues.
Cluster vectors plots (Figs. 3 and 4) show wavenumbers responsible for the
segregation in scores plot, and each distinguishing wavenumber corresponding to
specific biochemical assignment (Table 1 and 2). Mean band areas were calculated for
wavenumbers responsible for the differences in mean spectral absorbance. Peak
centroids were observed to shift to higher or lower wavenumbers, with significant
increase/decrease in the mean band areas (Table 3).
111
Fig 1. Mean spectra acquired from brain (A); kidney (B); heart (C); and, liver (D)
tissues of the African catfish (Heterobranchus bidorsalis) obtained in March 2013
from sampling points Ifiayong (Ify), Ikoro (Iko) and Gelegele (Geg) in the Niger Delta
region. Ify (solid black lines), Iko (broken black lines) and Geg (grey lines). Spectra
were cut between 1800 and 900 cm-1, baseline corrected and normalized to the Amide
I peak (1650 cm-1).
90011001300150017000.0
0.2
0.4
0.6
0.8
1.0
Wavenumber (cm-1)
Ab
so
rba
nc
e (
a.u
.)
90011001300150017000.0
0.2
0.4
0.6
0.8
1.0
Wavenumber (cm-1)
Ab
so
rba
nc
e (
a.u
.)
90011001300150017000.0
0.2
0.4
0.6
0.8
1.0
Wavenumber (cm-1)
Ab
so
rba
nc
e (
a.u
.)
90011001300150017000.0
0.2
0.4
0.6
0.8
1.0
Wavenumber (cm-1)A
bs
orb
an
ce
(a
.u.)
A
C
B
D
112
Fig 2. Principal component analysis coupled with linear discriminant analysis (PCA-
LDA) values in dataset acquired from Brain (A), Heart (B), Kidney (C) and Liver (D)
tissues of the African Catfish (Heterobranchus bidorsalis) obtained in March 2013
from sampling points Ifiayong (Ify), Ikoro (Iko) and Gelegele (Geg) within the Niger
Delta region. Spectra were cut between 1800 and 900 cm-1, baseline corrected and
normalized to the Amide I peak (~1750 cm-1). The Normalized spectra were mean
centred before the application of PCA-LDA. As determined using one-way ANOVA,
the PCA-LDA values in each class were statistically significant (P< 0.0001). Test
classes (Geg, Iko) were significant (P< 0.01) when compared to reference class (Ify)
using Dunnett's Multiple Comparison Test.
Ify
Iko
Geg
-0.4
-0.2
0.0
0.2
0.4
Dis
tan
ce i
n L
D1
Ify
Iko
Geg
-0.2
0.0
0.2
Dis
tan
ce i
n L
D1
Ify
Iko
Geg
-0.3
-0.1
0.1
0.3
Dis
tan
ce i
n L
D1
Ify
Iko
Geg
-0.6
-0.4
-0.2
-0.0
0.2
0.4
0.6
Dis
tan
ce i
n L
D1
A
C
B
D
113
Fig. 3. Cluster vector plots acquired from Brain (A), Heart (B), Kidney (C) and Liver
(D) tissues of the African Catfish (Heterobranchus bidorsalis) obtained in March
2013 from sampling points Ifiayong (Ify), Ikoro (Iko) and Gelegele (Geg) within the
Niger Delta region. Cluster vector plots were derived using Ifiayong as reference site.
Ify (Solid black lines), Iko (Broken black lines) and Geg (Grey lines). Spectra were
cut between 1800 and 900 cm-1, baseline corrected, Vector normalized and mean
centred before the application of multivariate analysis (PCA-LDA).
9001100130015001700-0.08
-0.03
0.02
0.07
*
***
*
*
*
**
*
* *
1674
15011474
16161620
1740
1747
12381142 1030
961
1146
Wavenumber (cm -1)
Co
eff
icie
nt
(a.u
.)
9001100130015001700-0.06
-0.01
0.04
**
* *
*
**
** *
*
*
14661570
1701
1616
1620
1524
1153
1215
1069
980
976
1022
Wavenumber (cm -1)
Co
eff
icie
nt
(a.u
.)
9001100130015001700-0.05
0.00
0.05
1717
***
**
*
1717
**
***
*
16781663
1616
1470
1466
1204
1543
155815241597
Wavenumber (cm -1)
Co
eff
icie
nt
(a.u
.)
9001100130015001700-0.3
-0.2
-0.1
-0.0
0.1
0.2
0.3
*
*
**
* *
*
***
17511732 1551
1639
1508
136614431470
**
1150
1219
1061
1173
Wavenumber (cm -1)
Co
eff
icie
nt
(a.u
.)
A B
C D
114
Fig. 4. Cluster vector plots acquired from Brain (A), Heart (B), Kidney (C) and Liver
(D) tissues of the African Catfish (Heterobranchus bidorsalis) obtained in March
2013 from sampling points Ifiayong (Ify), Ikoro (Iko) and Gelegele (Geg) within the
Niger Delta region. Cluster vector plots were derived using Ikoro as reference site. Ify
(Solid black lines), Iko (Broken black lines) and Geg (Grey lines). Spectra were cut
between 1800 and 900 cm-1, baseline corrected, Vector normalized and mean centred
before the application of multivariate analysis (PCA-LDA).
9001100130015001700-0.08
-0.03
0.02
0.07
**
*
*
**
1728
1740
16741620 1234
1501 **
**
*
*
964
961
1034
10301142
1146
Wavenumber (cm -1)
Co
eff
icie
nt
(a.u
.)
9001100130015001700
-0.05
0.00
0.05
*
*
*
*
**
1273
12151470
1616
15281655
**
**
*
1069
1069*976
1022
1018
1153
Wavenumber (cm -1)
Co
eff
icie
nt
(a.u
.)
9001100130015001700-0.05
0.00
0.05
*
*
*
*
*
*
**
*1470
1612
1616
1693
1678
17171543
1543*
*
*
1204
1169
1061
1647
Wavenumber (cm -1)
Co
eff
icie
nt
(a.u
.)
9001100130015001700
-0.15
0.00
0.15
*
*
*
*
*
*
1732
1751
1639
1470
1443
1508
*
*
**
*
1219
1150
1153
1061
1057
Wavenumber (cm -1)
Co
eff
icie
nt
(a.u
.)
A
D
B
C
115
Fig. 5. Mean spectra (A), Principal component coupled with Linear discriminant
analysis (PCA-LDA) scores (B) and cluster vector (C & D) plots acquired from Gill
tissues of the African Catfish (Heterobranchus bidorsalis) obtained in March 2013
from sampling points Ifiayong (Ify), Ikoro (Iko) and Gelegele (Geg) within the Niger
Delta region. Cluster vector plots were derived using Ifiayong (C) and Ikoro (D) as
reference sites. Ify (Solid black lines), Iko (Broken lines) and Geg (Grey lines).
Spectra were cut between 1800 and 900 cm-1, baseline corrected, Vector normalized
and mean centred before the application of multivariate analysis (PCA-LDA). As
determined using one-way ANOVA, the PCA-LDA values in each class were
statistically significant (p < 0.0001). Test classes (Geg, Iko) were significant P < 0.05
when compared to reference class (Ify) using Dunnett's Multiple Comparison Test.
90011001300150017000.0
0.2
0.4
0.6
0.8
1.0
Wavenumber (cm-1)
Ab
so
rba
nc
e (
a.u
.)
Ify
Iko
Geg
-0.2
0.0
0.2
Dis
tan
ce i
n L
D1
9001100130015001700-0.05
0.00
0.05
** *
***
* **
**
*
1740
1728
1651
1659
1504
1462
1408
1458
1589
1562
1612
1234
Wavenumber (cm -1)
Co
eff
icie
nt
(a.u
.)
9001100130015001700-0.08
-0.03
0.02 *
**
**
**
*
*
*
1504
1589
1659
1663
17401612
15621543
1462
1458
*
*
1173
968
Wavenumber (cm -1)
Co
eff
icie
nt
(a.u
.)
A
C
B
D
116
Table 1 Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy distinguishing wavenumbers as shown in cluster
vectors plots, and corresponding tentative chemical assignments: wavenumbers responsible for variance between tissue samples of the African
Catfish (Heterobranchus bidorsalis) obtained in March 2013 from sampling points Ifiayong, Ikoro and Gelegele within the Niger Delta region.
Distinguishing wavenumbers were derived following the application of multivariate analysis and using Ifiayong as reference site.
Sample Site Distinguishing
wavenumbers (cm-1) Tentative assignments References
Brain
Ikoro
1740 >C=O ester stretching vibrations in triglycerides 3
1620 Peak of nucleic acids due to the base carbonyl stretching and ring breathing mode 3
1501 In-plane CH bending vibrations from phenyl rings 1
1142 Phosphate and oligosaccharides; Oligosaccharide C – O bond in hydroxyl group
that might interact with some other membrane components 1
1030 Glycogen vibration; Collagen and phosphodiester groups of nucleic acids;
stretching C – O ribose. 1
961 C – O deoxyribose. 1
Gelegele
1747 C = O stretching vibration of Lipids, triglycerides , cholesterol esters 3
1674 Anti-parallel β-sheet of Amide I, v(C=C) trans, lipids, fatty acids 1
1616 Amide I (Carbonyl stretching vibrations in side chains of amino acids) 3
1474 Asymmetric CH3 bending of the methyl group of proteins 1
1238 Asymmetric PO2- stretching 1
1146 CO-O-C asymmetric stretching in glycogen and nucleic acids 3
Heart Ikoro
1616 Amide I (Carbonyl stretching vibrations in side chains of amino acids) 3
1215 PO2- asymmetric (Phosphate I) 1
1153 Stretching vibrations of hydrogen-bonding C – OH groups 1
1069 CO-O-C symmetric stretching of phospholipids and cholesterol esters 3
1022 Glycogen 1
976 OCH3 (polysaccharides, pectin ) 1
117
Gelegele
1701 Fatty acid esters 3
1620 Peak of nucleic acids due to the base carbonyl stretching and ring breathing mode 3
1570 Amide II 1
1524 Stretching C = N, C = C 1
1466 CH2 scissoring mode of acyl chain of lipid, Cholesterol-methyl band 1
980 OCH3 (polysaccharides-cellulose) 1
Kidney
Ikoro
1717 Amide I (arises from C = O stretching vibration), C = O stretching vibration DNA
and RNA. 1
1678
Stretching C = O vibrations that are H-bonded (changes in the C = O stretching
vibrations could be connected with destruction of old H-bonds and creation of the
new ones).
1
1616 Amide I (Carbonyl stretching vibrations in side chains of amino acids) 3
1543 Amide II 1
1470 CH2 bending of the methylene chains in lipids 1
1204 Vibrational modes of collagen proteins-amide III C – O – C, C – O dominated by
the ring vibrations of polysaccharides C – O – P, P – O – P collagen. 1
Gelegele
1717 Amide I (arises from C = O stretching vibration), C = O stretching vibration DNA
and RNA. 1
1663 Amide I band, v(C=C) cis, lipids, fatty acids 3
1597 C = N, NH2 adenine 1
1558 Ring base 1
1524 Stretching C = N, C = C 1
1466 CH2 scissoring mode of the acyl chain of lipid 1
118
Liver
Ikoro
1751 v(C=C) lipids, fatty acids 3
1443 δ(CH2), lipids, fatty acids 1
1366 Stretching C – O, deformation C – H, deformation N – H. 1
1219 PO2
- asymmetric vibrations of nucleic acids when it is highly hydrogen bonded,
asymmetric hydrogen-bonded phosphate stretching mode 1
1150 C – O stretching vibration, C – O stretching mode of the carbohydrates CH8 1
1061 CO – O – C symmetric stretching of phospholipids and cholesterol esters 3
Gelegele
1732 C = O stretching in lipids 1
1639 Amide I 1
1443 δ(CH2), lipids, fatty acids, Asymmetric CH3 bending of the methyl groups of
proteins 3
1551 Amide II of proteins, N – H bending and C – N stretching 1
1470 CH2 bending of the methylene chains in lipids 1
1173 C – O (stretching in malignant tissues), Non- hydrogen-bonded stretching mode of
C – OH groups 1
Gills
Ikoro
1740 >C=O ester stretching vibrations in triglycerides 3
1659 Amide I 1
1612 Amide I (Carbonyl stretching vibrations in side chains of amino acids) 3
1562 CO2- asymmetric stretching possibly from glutamic acid 2
1504 In-plane CH bending vibrations from the phenyl rings 1
1458 CH3 asymmetric bending 2
Gelegele
1728 C = O band/ stretching 1, 2
1651
80% C = O stretching; 10% C – N stretching; 10%
N – H bending, Amide I absorption (predominantly the C = O stretching vibration
of the amide C = O)
1,2
1589 Ring C – C stretch of phenyl, Ring stretching vibrations with little interaction with 3
119
CH in-plane bending
1462 CH2 scissoring 2
1408 CH3 asymmetric deformation, (CH3)3N+ symmetric bending 1, 2
1234 Amide III/phosphate vibration of nucleic acids 1
v: stretching, δ: deformation
References: (1) Movasaghi et al., 2008; (2) Stuart, 2005; (3) Obinaju et al., 2014
120
Table 2 Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy distinguishing wavenumbers as shown in cluster
vectors plots, and corresponding tentative chemical assignments: wavenumbers responsible for variance between tissue samples of the African
Catfish (Heterobranchus bidorsalis) obtained in March 2013 from sampling points Ifiayong, Ikoro and Gelegele within the Niger Delta region.
Distinguishing wavenumbers were derived following the application of multivariate analysis and using Ikoro as reference site.
Sample Site Distinguishing
wavenumbers (cm-1) Tentative assignments References
Brain
Ifiayong
1740 >C=O ester stretching vibrations in triglycerides 3
1620 Peak of nucleic acids due to the base carbonyl stretching and ring breathing mode 3
1501 In-plane CH bending vibrations from phenyl rings 1
1142 Phosphate and oligosaccharides; Oligosaccharide C – O bond in hydroxyl group that
might interact with some other membrane components 1
1030 Glycogen vibration; Collagen and phosphodiester groups of nucleic acids; stretching
C – O ribose. 1
961 C – O deoxyribose. 1
Gelegele
1728 C = O band/ stretching 1, 2
1674 Anti-parallel β-sheet of Amide I, v(C=C) trans, lipids, fatty acids 1
1234 Amide III/phosphate vibration of nucleic acids 1
1146 CO-O-C asymmetric stretching in glycogen and nucleic acids 3
1034 Collagen 1
964 C – C, C – O deoxyribose 1, 3
Heart Ifiayong
1616 Amide I (Carbonyl stretching vibrations in side chains of amino acids) 3
1215 PO2- asymmetric (Phosphate I) 1
1153 Stretching vibrations of hydrogen-bonding C – OH groups 1
1069 CO-O-C symmetric stretching of phospholipids and cholesterol esters 3
1022 Glycogen 1
976 OCH3 (polysaccharides, pectin ) 1
121
Gelegele
1655 Amide I of proteins in α-helix conformation,
Amide I (v C = O, δ C – N, δ N – H 1
1528 Stretching C = N, C = C 1
1470 CH2 bending of the methylene chains in lipids 1
1273 CHα rocking 1, 2
1069 CO-O-C symmetric stretching of phospholipids and cholesterol esters 3
1018 DNA ribose C–O stretching
RNA ribose C–O stretching 2
Kidney
Ifiayong
1717 Amide I (arises from C = O stretching vibration), C = O stretching vibration DNA
and RNA. 1
1678
Stretching C = O vibrations that are H-bonded (changes in the C = O stretching
vibrations could be connected with destruction of old H-bonds and creation of the
new ones).
1
1616 Amide I (Carbonyl stretching vibrations in side chains of amino acids) 3
1543 Amide II 1
1470 CH2 bending of the methylene chains in lipids 1
1204 Vibrational modes of collagen proteins-amide III C – O – C, C – O dominated by the
ring vibrations of polysaccharides C – O – P, P – O – P collagen. 1
Gelegele
1693
A high frequency vibration of an antiparallel β-sheet of amide I ( the amide I band is
due to in-plane stretching of the C = O band weakly coupled to stretching of the C –
N and in-plane bending of the N – H bond
1
1647 Amide I (α-helix) 2
1612 Amide I (Carbonyl stretching vibrations in side chains of amino acids) 3
1543 Amide II 1
1169 vas CO – O – C 1
1061 CO – O – C symmetric stretching of phospholipids and cholesterol esters 3
122
Liver
Ifiayong
1751 v(C=C) lipids, fatty acids 3
1443 δ(CH2), lipids, fatty acids 1
1219 PO2
- asymmetric vibrations of nucleic acids when it is highly hydrogen bonded,
asymmetric hydrogen-bonded phosphate stretching mode 1
1150 C – O stretching vibration, C – O stretching mode of the carbohydrates CH8 1
1061 CO – O – C symmetric stretching of phospholipids and cholesterol esters 3
Gelegele
1732 C = O stretching in lipids 1
1639 Amide I 1
1508 In-plane CH bending vibration from the phenyl rings 1
1470 CH2 bending of the methylene chains in lipids 1
1153 Stretching vibrations of hydrogen-bonding C – OH groups 1
1057 Stretching C – O deoxyribose 1
Gills
Ifiayong
1740 >C=O ester stretching vibrations in triglycerides 3
1659 Amide I 1
1612 Amide I (Carbonyl stretching vibrations in side chains of amino acids) 3
1562 CO2- asymmetric stretching possibly from glutamic acid 2
1504 In-plane CH bending vibrations from the phenyl rings 1
1458 CH3 asymmetric bending 2
Gelegele
1663 Amide I band, v(C=C) cis, lipids, fatty acids 3
1589 Ring C – C stretch of phenyl 1
1543 Amide II (protein N-H bend, C-N stretch) in α-helices 3
1462 CH2 scissoring 2
1178 C-O asymmetric stretching of glycogen 3
968 C – C , C – O deoxyribose, DNA 1
v: stretching, δ: deformation, References: (1) Movasaghi et al., 2008; (2) Stuart, 2005; (3) Obinaju et al., 201
123
Table 3 Mid-infrared absorbance peak/band areas calculated for each detected centroid in mean bio-fingerprint spectra of the African catfish
(Heterobranchus bidorsalis) tissues samples collected from Ifiayong (Ify), Ikoro (Iko) and Gelegele (Geg) in March 2013, along Ovia River
(Nigeria). Peak/band areas are representative of concentrations of biomolecule within the tissue. Peak areas were statistically tested using
Ifiayong as the control.
Tissue Band Assignment Peak Centroid Peak Area
IFY IKO GEG IFY IKO GEG
Brain
Lipids 1740 Not
observed ¶ 1736 5.64 ± 1.36 2.29 ± 1.04 *** 4.58 ± 1.58 *
Amide I 1643 1632 1643 20.92 ± 0.60 21.03 ± 0.38 20.88 ± 0.61
Amide II 1535 1528 1535 14.45 ± 0.77 16.15 ± 0.99 *** 14.71 ± 1.20
COO- symmetric stretch in fatty and amino acids,
CH2
and CH3 deformation in lipids and protein
Not
observed ¶ 1393
Not
observed ¶ 4.4 ± 0.38 3.9 ± 0.33 *** 4.4 ± 0.20
Amide III/Phosphate Vibrations of Nucleic Acids 1231 1231 1231 11.11 ± 1.74 7.33 ± 1.01 *** 11. 02 ± 2.03
Stretching / Vibrations of C – O ribose and
Deoxyribose 1061 1065 1057 15.67 ± 3.89 9.26 ± 2.77 *** 15.31 ± 4.56
Liver
Lipids Not
observed ¶ 1740 1740 2.22 ± 1.46 6.09 ± 2.02 *** 3.60 ± 2.30 **
Amide I 1628 1628 1628 21.42 ± 0.41 21.65 ± 0.40 * 20.79 ± 0.45 ***
Amide II 1520 1528 1531 16.61 ± 0.47 15.60 ± 1.11 *** 14.97 ± 0.83 ***
COO- symmetric stretch in fatty and amino acids,
CH2
and CH3 deformation in lipids and protein
1389 Not
observed ¶
Not
observed ¶ 6.50 ± 0.41 7.05 ± 0.39 *** 6.09 ± 0.38 ***
Amide III/Phosphate Vibrations of Nucleic Acids 1234 1234 1234 7.73 ± 1.05 9.51 ± 1.12 *** 7.89 ± 1.20
C – O Stretching Not
observed 1173 1173 4.03 ± 1.04 6.81 ± 1.45 *** 4.87 ± 1.66
124
Stretching / Vibrations of C – O ribose and
Deoxyribose 1065
Not
observed ¶
Not
observed ¶ 8.33 ± 1.35 9.46 ± 1.40 *** 8.65 ± 1.40
Heart
Amide I 1628 1632 1632 21.28 ± 0.30 20.85 ± 0.31 *** 20.56 ± 0.56 ***
Amide II 1524 1524 1531 17.27 ± 0.85 17.28 ± 0.71 17.13 ± 0.60 *
COO- symmetric stretch in fatty and amino acids,
CH2
and CH3 deformation in lipids and protein
1393 1393 1393 6.87 ± 0.30 6.65 ± 0.31 * 6.23 ± 0.44 ***
Amide III/Phosphate Vibrations of Nucleic Acids 1234 1238 1234 7.60 ± 1.00 6.48 ± 0.62 *** 6.57 ± 0.56 ***
Glycogen Not
observed ¶ 1026
Not
observed ¶ 8.70 ± 3.22 9.07 ± 3.01 6.35 ± 1.37 ***
Kidney
Amide I 1628 1628 1632 22.34 ± 0.54 21.78 ± 0.31 *** 21.59 ± 0.33 ***
Amide II 1528 1520 1520 16.53 ± 1.13 16.64 ± 0.64 17.14 ± 0.66 *
COO- symmetric stretch in fatty and amino acids,
CH2
and CH3 deformation in lipids and protein
Not
observed ¶ 1389 1389 7.42 ± 0.72 6.91 ± 0.36 ** 6.87 ± 0.46 ***
Amide III/Phosphate Vibrations of Nucleic Acids 1234 1234 1234 9.71 ± 1.87 8.48 ± 1.03 ** 7.97 ± 0.98 ***
C – O Stretching 1169 Not
observed ¶ 1173 6.11 ± 2.08 4.72 ± 1.24 * 4.10 ± 1.12 ***
Gills
Amide I 1628 1628 1628 21.57 ± 0.25 21.07 ± 0.39 *** 21.66 ± 0.15
Amide II 1520 1528 1531 17.56 ± 0.39 16.86 ± 0.49 *** 17.42 ± 0.44 *
COO- symmetric stretch in fatty and amino acids,
CH2
and CH3 deformation in lipids and protein
1389 1393 1389 6.97 ± 0.32 6.42 ± 0.41 *** 7.15 ± 0.43 *
Amide III/Phosphate Vibrations of Nucleic Acids 1234 1234 1234 6.82 ± 0.28 6.16 ± 0.39 *** 6.90 ± 0.51
***p < 0.0001, **p > 0.001, * p > 0.05; ¶ not observed: peak centroid not detected.
125
4. Discussion
The mean absorbance spectra present an overview of the changes occurring in an
interrogated sample. These changes are recognised as intensity variations and shifts in
peak centroids. The intensity of absorption bands in the IR spectra of biological
samples is regarded as directly proportional to the concentration of the particular
biomolecule (Cakmak et al., 2006; Severcan et al., 2005). The position of peak
centroids such as Amide I (~1650 cm-1) is considered to be sensitive to protein
conformation (Obinaju et al., 2015; Palaniappan and Pramod, 2011) and thus, peak
shifts are regarded as alterations to either total structure of the molecule or specific
peptides (Holman et al., 2000; Obinaju et al., 2015). Within the scores plots, nearness
of the individual sites to each other suggests a similarity of chemical structures and
distance suggests dissimilarity. A positive index in LD1 suggests an increase in the
total biomolecules present in the sample compared (Llabjani et al., 2014).
Based on the physical characteristics of the various sites, initial cluster vectors
plots (Fig. 3) compared tissues sampled from Gelegele and Ikoro to tissues from
Ifiayong. Spectral differences were observed in cluster vectors plots associated with
lipid/protein (~1750 cm-1 - 1400 cm-1) and DNA/RNA (~1300 cm-1 - 900 cm-1)
regions of the biofingerprint of all tissue types. These included alterations to C=O
stretching vibrations in triglycerides and cholesterol esters, carbonyl stretching
vibrations in the side chains of amino acids, asymmetric stretching of phosphate and
carbon-to-oxygen vibrations in deoxyribose. However, from observations in mean
absorbance spectra, and the differences between the sites in 1-D scores plots, a second
cluster vectors plot (Fig 4) compared tissues sampled from Gelegele and Ifiayong to
tissues from Ikoro. These plots revealed a similar pattern of spectral alterations across
the IR fingerprint of the tissues from Gelegele and Ifiayong, with slight variations to
126
intensity in tissues obtained from Gelegele. Spectral alterations in cluster vectors plots
closely matched the observations in the mean absorbance spectra for tissues sampled
from Gelegele and Ifiayong, compared to Ikoro. This observation suggests the
possibility of environmental contamination occurring at Ifiayong. More importantly, it
suggests that the alterations in tissues from Ifiayong were induced by contaminants
possibly similar to those present at Gelegele (e.g., PAHs). Tissues from Ifiayong and
Ikoro were consistently distinguished by the same wavenumbers in cluster vectors
plots. In contrast, distinguishing wavenumbers varied for tissue samples from
Gelegele, when compared to tissues from either Ifiayong or Ikoro in the cluster
vectors plots.
From previous observations (Obinaju et al., 2014; Obinaju et al., 2015) and the
site characteristics of Gelegele, alterations observed within the DNA/RNA region of
the biofingerprint are possibly PAH-mediated toxicity as a result of metabolite
binding to macromolecules such as DNA. With no documentation of industrial
activity in close proximity to Ifiayong, we hypothesize that the possibility of
contamination occurring at Ifiayong may be due to 1) the sloppy topography of the
community which predisposes it to erosions (Umoh, 2013) and the possible deposition
of contaminants from urban runoff; or, 2) the location of the community along a river
path, e.g., downstream, making it a recipient of environmental contaminants based on
the direction of river flow. It is also possible that fish samples obtained at Ifiayong
were pre-exposed and migrated from contaminated regions as a measure of adaptation
to environmental change (Alemanni et al., 2003).
127
5. Conclusion
FTIR spectroscopy monitors the vibrational modes of functional groups within
biomolecules and enables a correlation between chemical information and histological
structures, where shifts in peak positions, changes in bandwidths, intensities and band
area values of the IR bands are used to obtain valuable structural and functional
information about the system of interest (Cakmak et al., 2006). We have previously
shown the potential of ATR-FTIR spectroscopy to detect sub-lethal real-time exposure
to environmental contaminants in sentinel organisms (Obinaju et al., 2014). More
recently, we have shown that the patterns of spectral alterations in the IR spectra
signature can be related to the presence of specific environmental contaminants
(Obinaju et al., 2015).
This study aimed to understand the changes occurring in fish tissues as a result
of PAH exposure at the sites Gelegele and Ikoro, by comparing the IR spectra of the
tissues to those obtained from a relatively pristine site (Ifiayong). This aim was
modified based on observations in the mean absorbance spectra of the tissues
interrogated, and Ifiayong was classed as a ‘blind’ site (i.e., site with no prior
information of contaminant levels or recorded history of contamination). Multivariate
analysis revealed that PAH contamination could be occurring at Ifiayong. Although
contaminant bioaccumulation in the tissues of most sentinels may not pose a direct
health risk to the human population, monitoring bioaccumulation in these tissues are
important to assessing environmental health of most ecosystems. Our results present
the possibility of identifying contaminants and contaminant-induced changes in
organisms of unknown origins, based on existing knowledge of IR spectra acquired
from organisms with exposure to known compounds. Our results provide evidence
supporting the use of ATR-FTIR spectroscopy in biomonitoring in sentinel organisms.
128
ACKNOWLEDGEMENTS: BEO is a Faculty for the Future Fellow of the
Schlumberger Foundation, an independent non-profit entity that supports science and
technology education. Such Fellowships support female academics from developing
and emerging countries for advanced graduate study.
129
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Chapter 7
Discussion
Ecotoxicology primarily aims to identify patterns describing population and
community responses to contaminants as it is now generally acceptable that chemical
characterization of a compound by itself does not provide specific biological
information about potential hazards to organisms (Chapman, 2007). The ability of
researchers to predict these responses is generally greatest for communities that
change consistently in response to a specific contaminant or class of contaminants,
thereby providing a direct path to extrapolation, hypothesis testing, and scientific
inference (Clements et al., 2012). Validated biomarkers are important tools for
ecotoxicologists where they are early warning signals to pre-empt dire environmental
consequences (Eason and O'Halloran, 2002). As sensitive and ecologically relevant
measures of environmental conditions, bioindicators can be used to assess the health
of aquatic ecosystems which may be compromised by a variety of environmental
stressors such as contaminants, sediments, nutrients, and varying temperature, salinity,
and hydrologic regimes (Adams and Greeley, 2000).
The bioindicator approach uses responses of key (sentinel) aquatic organisms
both as integrators of stress effects and as sensitive response (early-warning)
indicators of environmental health (Phillips and Rainbow, 1993). It involves
measuring a suite of selected biological and ecological responses at several levels of
biological organization from the biomolecular and biochemical to the community
levels (Bodin et al., 2011; Brown et al., 1973; 1977; Malins, et al., 1984). In the
context of environmental monitoring studies bioindicators describe organisms (or
147
parts of organisms or communities of organisms) that provide information on quality
of the environment (or a part of the environment) (Markert et al., 2003).
Environmental contamination occurs or is more frequently encountered as
complex mixtures and the behaviour of chemicals in a mixture rarely corresponds to
that predicted from data on the pure compounds (Llabjani et al., 2010). Relating low
environmental exposures to actual effects in organisms is often difficult and requires
lab-based dose response type assays often involving sophisticated techniques and
protocols requiring expensive kits. This understanding creates the need for simple,
cost effective yet highly sensitive techniques and robust protocols (Baker et al., 2014)
that are applicable to environmental biomonitoring and able to detect real-time
contaminant exposure in these organisms even at very low doses.
Molecular bonds with an electric dipole moment that can change by atomic
displacement owing to natural vibrations are IR active and the various vibrational
modes can be quantitatively measured using vibrational spectroscopy (Griffiths and
De Haseth, 2007). Vibrational spectroscopic techniques especially FTIR spectroscopy
techniques, have become potential tools for non-invasive optical tissue diagnosis and
have been applied to study a wide variety of pathologic states especially in clinical
fields. A variety of the techniques have been applied to study chemical induced
toxicity in sentinels (Cakmak et al., 2006; Llabjani et al., 2012; Severcan et al., 2005;
Toyran et al., 2004).
The application of spectroscopy to biological samples generates information,
based on the vibrational modes of functional groups within biomolecules and enables
the correlation between chemical information and histological structures. The IR
absorbance spectra for any biological sample contains important information
148
regarding the structure and conformation of molecules within the sample. The IR
spectra with wavenumbers from 1800 – 900 cm-1 is regarded as the biochemical cell
fingerprint of a biological sample, with each wavenumber corresponding to a specific
biological molecule (Baker et. al., 2014).
Each acquired spectrum consists of hundreds of variables and therefore
requires the use of computational analyses to extract the required information. There
are several computational approaches that are potentially applicable to spectral
datasets. However, the most commonly used include PCA, LDA, PLS and PCA-LDA.
These techniques allow for the derivation of possible biomarkers (Fig. 8) which
distinguish between sample types and treatment conditions.
149
7.1 Understanding chemical induced changes in cells and potentially subcellular
components of cells
Obinaju et. al. (2015a) illustrates the potential application of ATR-FTIR
spectroscopy to study, signature and understand the changes occurring in subcellular
components of the cell as a result of exposure to potential mutagens. Chemically
induced changes to biological molecules, particularly genetic molecules such as DNA
are known to predispose organisms to pathologic disease conditions e.g. cancers
(Malins et. al. 2006). The ability of infrared spectroscopy techniques to distinguish
intact cell and subcell i.e. nucleus has been previously shown (Pang et al., 2012;
Pijanka et al., 2009). The ability to detect slight changes in the IR spectra of samples
at wavenumbers representative of biomolecules, e.g., symmetric (1088 cm-1) and
asymmetric (1234 cm-1) PO-2 bands, which typically can be associated with nucleic
acids, are significant for understanding the differences between normal and malignant
conditions (Lasch et al., 2002). Study presented in chapter three employed the use of
ATR-FTIR spectroscopy techniques to detect and understand the response of cultured
cell populations of the human mammary carcinoma (MCF-7), to very low dose
exposures of B[a]P. The project observed the effect of exposure in both intact cells
and isolated nuclei of cells in the G0/G1 phase of the cell cycle.
In vitro experiments using very low doses (< 10-4 M) are more realistic models
to concentrations obtainable in real world scenarios. Isolated nuclei of cells treated
with the 10-6 M of B[a]P were observed to be slightly deformed and observed effects
were attributed to the possible biotransformation/activation of B[a]P as well as its
interaction with DNA molecules as previously documented by Malins et.al. (2006).
B[a]P induced dose dependent alterations in cell populations in G0/G1 phase and a
bimodal response in S-phase. All observed responses were statistically significant (p <
150
0.01) when compared to control cell populations. The ability to extract potential
biomarkers as wavenumbers using multivariate analysis was important to
distinguishing each treatment condition from the control.
Wavenumbers such as 1740 cm-1 in isolated nuclei of the treated cell
population suggested that B[a]P induced changes to nuclear lipids of isolated nuclei of
the cells (Balasubramanian et al., 2007). Nuclear lipids are believed to play a role in
the proliferation, differentiation and apoptotic processes in the cell cycle and changes
to nuclear lipids may be one of the mechanisms by which high doses of B[a]P exerts
cytotoxicity/cell death (Ledeen and Wu, 2006; Lin and Yang, 2008). C = O guanine
deformation was also identified in isolated nuclei of G0/G1 cells. This could be
regarded a potential biomarker for B[a]P exposure in cells as B[a]P metabolites are
known to possess a high affinity for guanine and covalently alter the structure of this
molecule. Thus, understanding chemical induced changes in the various phases of the
cell cycle is important for extrapolations to the changes occurring in quiescent and
rapidly dividing cell populations e.g. neurons and epithelial cell populations.
7.2 IR spectroscopy to study real-time exposure in organisms
The ability to identify changes to cell and sub-cell using ATR-FTIR spectroscopy as
documented in Obinaju et.al. (2015a) as well as previous applications particularly in
clinical diagnostics to differentiate between normal and diseased tissues (Wong et al.,
1991; Fung et al., 1996) and cell types (German et al., 2006) has informed the
application of ATR-FTIR in environmental toxicology, particularly to track and
understand effects of environmental compounds in real-time.
Impact and consequences of environmental contamination in Nigeria on the
resident population is fairly known due to the scanty literature on risk assessment
151
studies, (eco) toxicological studies or epidemiology. Although there are studies
documenting PAH contamination within the Niger Delta, these studies document
mainly concentrations of PAHs in particle and sediment phase of the water column
(Ana et al., 2009; Essien et al., 2011; Okafor and Opuene, 2007) and no study
documenting concentrations in dissolved phase.
Compared to studies monitoring the bioaccumulation of heavy and trace
metals in sentinels, very few studies (Anyakora and Coker, 2007; Benson et al., 2008;
Eduok et al., 2010) have attempted to monitor PAHs in sentinels within the Niger
Delta region. Furthermore, these studies only document the concentrations of PAHs
within sentinel tissues with no specific documentation of possible exposure effects,
e.g., DNA damage in the observed sentinels as a response to PAH exposure.
Taking these knowledge gap into account, real-time exposure effects in the
tissues of the African catfish (Heterobranchus bidorsalis) and the water spinach
(Ipomea aquatica) were observed and the first evidence of concentration levels for
PAHs in the dissolved phase of a river in the Niger Delta presented (Obinaju et.al
2015b; Obinaju et. al. 2014). The studies employed ATR-FTIR spectroscopy
combined with multivariate analysis to signature effect and extract the possible
biomarkers in the various tissues. Effect of contaminant exposure was observed in fish
tissues sourced along the Ovia River which plays host to petroleum exploration
activities and as such is thought to be contaminated with PAHs.
152
7.3 Differentiating between samples of varying exposure conditions using ATR-
FTIR
Obinaju et. al. (2014) documents that in both sampling seasons (dry and rainy), ATR-
FTIR mean spectra was able to detect subtle but clear variations between sampling
sites. The application of multivariate analysis (PCA-LDA) to the bio-fingerprint
region derived scores plots and corresponding cluster vector plots for the various
tissues interrogated and multivariate analysis was able to make clear distinctions
between the tissues based on seasonal variation.
Increased concentration of ester groups belonging to triglycerides within
exposed tissues, particularly in the liver was observed by the increased intensity of
lipid peaks in liver tissues samples. This observation was more remarked in liver
tissues obtained in the dry season and was possibly due to an increased concentration
of PAHs available for absorption due to reduced river current. The increase absorption
of the available PAH compounds and subsequent metabolism in the liver may possibly
have induced changes in lipid metabolism, resulting in the accumulation of lipids
within the liver tissues and possibly the onset of fatty liver. Metabolic activities of
enzymes in liver tissues resulted in changes to intensity and area of glycogen band
(1177 cm-1), a possible measure of oxidative stress in liver tissues. These findings
were in agreement with chemical induced changes previously documented to have
occurred in rainbow trout following exposure to 17β-estradiol (Cakmak et. al. 2006).
In plant leaves, senescence and environmental stresses are accompanied by
changes in the cell surfaces and pigments which determine the optical characteristics
of plant tissues (Ribeiro da Luz, 2006). Changes may arise from age of leaves and/or
season variation. Obinaju et.al. (2014) was able to correlate variation in leaf
153
pigmentation with the characteristics of the sampling site after sample fixation. These
variations were very well explained by the intensity variations to absorption band for
cellulose (1030 cm-1) in the IR spectra of the individual biofingerprint of the samples.
Changes to absorption bands between absorption bands between 1650 cm-1 and 1500
cm-1 were considered physiological stress markers in plant leaves as previously
suggested in Ivanova and Singh (2003). IR spectroscopy’s ability to detect the subtle
changes in structure and dynamics of biological molecules in sentinel organisms
exposed to varying degrees of environmental contamination presents the possibility
for real-time evaluation of contaminant toxicity and could be important to effective
environmental monitoring.
7.4 PAH concentration relation to IR biofingerprint
There is no literature documenting the concentrations of PAHs in the dissolved phase
of the water column of any of the aquatic environments within the Niger Delta. Hence,
Obinaju et. al. (2015b) measured PAH concentrations in the dissolved phase of the
water column of the Ovia River and report concentrations ranging from 0.1 - 1055.6
ng.L-1 for both dry and rainy seasons. Detected concentrations decreased with
increased distance from the known pollution source. Seasonal influence (e.g., heavy
rainfall), which causes a change in river current and influences the dispersal of
compounds was suspected as a possible factor responsible for the increased
concentrations of compounds such as 2,6-dimethylnaphthalene, 2,3,6-
trimethylnaphthalene, phenanthrene, fluorene, anthracene, 1-methylphenanthrene,
fluoroanthene, pyrene at relatively pristine sites in the rainy season.
The concentrations of PAH detected in Ovia river were either similar or much higher
than concentrations detected in the dissolved phases of the water column in
154
comparable locations such as Pearl River and Macao harbour of the Pearl River Delta
in South China, the Seine Estuary in France and the Southern Chesapeake Bay, USA
(Cailleaud et al., 2007; Gustafson and Dickhut, 1997; Luo et al., 2004).
In order to exhibit carcinogenicity, chemical carcinogens require metabolic
activation by cytochrome P450 (CYP) enzymes to more reactive metabolites. The
liver of most organisms is the primary site of metabolic activation. Thus, a
comparison of mean absorbance spectra and cluster vector plots for B[a]P treated
MCF-7 cells to those of H. bidorsalis liver tissues was used to determine if alterations
observed in fish tissues were PAH-induced. The mean absorbance spectra showed
similar patterns of alterations in B[a]P treated MCF-7 cells and fish liver tissue. These
alterations were observed in Amide I (1650 cm-1), Amide II (1550 cm-1) and Amide
III/asymmetric phosphate stretching vibrations of nucleic acids (1234 cm-1) regions of
the biofingerprint. Peak areas calculated for each detected centroid in the mean spectra
biofingerprint of interrogated H. bidorsalis tissues showed statistically significant
decrease and increase in most band areas across all tissues interrogated and centroid
positions of most peaks were observed to shift to lower or higher values in tissues
interrogated. Based on the known source of contaminants, the results suggested that
alterations in the biofingerprint spectra of tissues particularly to regions representative
of proteins (Amide I and II) were PAH induced, a possible consequence of PAH-
induced protein oxidation in the tissues and possibly mediated by ROS.
155
Fig. 8 Summary of the various mechanisms of genotoxic and non-genotoxic
carcinogenic environmental compounds, including polycyclic aromatic hydrocarbons.
Compounds directly or indirectly affect the regulation and expression of genes
involved in cell cycle control, DNA repair, cell differentiation or apoptosis (cell
death). DNA damage or altered signal transduction processes may lead to the loss of
growth control and genome instability, the major hallmarks of cancer.
156
7.5 Identification of contaminant exposure in tissues based on IR spectra.
Both Obinaju et. al., (2014) and Obinaju et. al., (2015b) all showed that IR
spectroscopy, particularly ATR-FTIR spectroscopy was able to differentiate between
real-time exposure effects in tissues from sites with varying degrees of environmental
contamination in both animal and plant tissues based on spectral variation. Thus,
comparing tissues of the African Catfish (Heterobrachus bidorsalis) from sites with a
documented history of polycyclic aromatic hydrocarbon (PAH) contamination from
petroleum exploration activities, to tissues from a relatively pristine site with no
documented history of contamination with PAHs and no industrial activity, Obinaju
and Martin in chapter 6 aimed to show that it was potentially possible to determine the
nature of compounds present at a site, based on spectra similarity or difference.
The mean absorbance spectra for tissues showed relatively marked alterations
within the DNA/RNA region, as well as alterations to lipid and protein regions of the
biofingerprint for all tissues excluding gills tissues. These alterations were reflected as
increased or reduced intensities in the affected spectral region of the tissues. As have
been previously suggested, the intensity and/or the area of the absorption bands is
directly related to the concentration of the molecules (Cakmak et al., 2006; Severcan
et al., 2005; Toyran et al., 2004).
However, the mean absorbance for most tissues (brain, liver, and gills)
sampled from the relatively pristine site were more similar to the mean absorbance for
tissues sampled from the site regarded as most contaminated based on detected PAH
concentrations in previous projects (Obinaju et. al., 2015b) and the industrial activity
at site. Spectral differences were observed in cluster vector plots associated with
lipid/protein (~1750 cm-1 - 1400 cm-1) and DNA/RNA (~1300 cm-1 - 900 cm-1)
regions of the biofingerprint of all tissue types. These included alterations to C = O
157
stretching vibrations in triglycerides and cholesterol esters, carbonyl stretching
vibrations in the side chains of amino acids, asymmetric stretching of phosphate and
carbon to oxygen vibrations in deoxyribose. Cluster vector plots which compared
tissue samples from both pristine and most contaminated sites to tissues obtained from
a site of known lesser contamination revealed a similar pattern of spectral alterations
across the IR fingerprint of the tissues from pristine and most contaminated site, with
slight variations to intensity in tissues obtained from the most contamination.
Based on the known characteristics of the pristine site, the possibility of
contamination occurring may be due to the sloppy topography of the site which
predisposes it to erosions, or the location of the site along a river path e.g.
downstream, making it a recipient of environmental contaminants based on the
direction of river flow. There is also the possibility that fish samples obtained were
pre- exposed and had migrated from contaminated regions as a measure of adaptation
to environmental change. Whichever the case, the ability of IR spectroscopy and
multivariate analysis to discriminate these exposures in the tissues illustrates the
immense potential of vibrational spectroscopy especially the techniques involved in
IR spectroscopy, in environmental biomonitoring.
158
Conclusion
IR spectroscopy monitors the vibrational modes of functional groups within
biomolecules and enables a correlation between chemical information and histological
structures. The bands within an IR spectrum are used to obtain valuable structural and
functional information about the system of interest. The various methods employed in
IR spectroscopy are specific and sensitive to changes such as shifts in peak positions,
changes in bandwidths, intensities and band area values within the biochemical
constituents of cells and tissues at certain wavelengths. These changes can be
correlated to the exposure to specific chemicals and potential biomarkers can be
extracted based on the variation to specific IR bands.
Every study documented herein was designed to test the following hypotheses
1) the technique ATR-FTIR is sensitive to and able to detect minimal cellular changes
occurring in tissues exposed to potential mutagens. 2) ATR-FTIR can extract potential
biomarkers to signature chemical induced changes in tissues. Studies were designed to
investigate the effects of low dose PAH exposure in intact cells and isolated nuclei as
a baseline for real-time environmental exposure scenarios, to detect seasonal
variations in exposure and real-time exposure effect in fish tissue and plant leaves, to
investigate the correlation of PAH concentrations detected in dissolved phase of water
column to the alterations in fish tissues and to explore the potential identification of
biomarkers of PAH exposure in fish tissues using ATR-FTIR spectroscopy.
Results show that ATR-FTIR was able to detect changes in cell populations exposed
to very low doses of B[a]P including the treatment-induced changes in the nuclei of
the exposed population. The study investigating the possible application of ATR-
FTIR to observe contaminant effect in sentinels showed that the technique was able to
159
detect real-time sub-lethal exposures in the tissues of organisms studied and the
variations to spectral regions of the biofingerprint were reflective of the
concentrations of known and detected contaminants at the various sampling sites.
ATR-FTIR was able to extract of wavenumbers (potential biomarkers) which are
representative of biomolecules that were possibly chemically altered. ATR-FTIR was
able to detect exposure in tissues obtained without prior knowledge of contaminant
exposure or the nature of the possible contaminating compound and presented the
possibility of describing the nature of compound present.
Retrospectively, a single year of sampling may not be the best possible
representation of long term contaminant effects in organisms. It would have been
interesting to measure a variety of potential contaminants, including metals in the
different phases of the water column. The sampling size could definitely be increased
and expanded to account for changes occurring as a result of sex and lifecycle in the
organisms studied. These are areas that could be improved in subsequent experimental
designs given that projects discussed herein are the first of its kind to be conducted
within the region.
That said and finally, vibrational spectroscopy is becoming a valuable tool to
understand molecular pathways and a potential tool for clinical diagnosis. Its
application in other areas such as agriculture and environmental monitoring hold equal
promise. Technological advancement and the optimisation of protocols hold these
promises for even more applications of vibrational spectroscopy in both biological and
other scientific studies.
160
Appendix I
Using Fourier transform IR spectroscopy to analyze biological materials
Matthew J. Baker, Júlio Trevisan, Paul Bassan, Rohit Bhargava, Holly J. Butler,
Konrad M. Dorling, Peter R. Fielden, Simon W. Fogarty, Nigel J. Fullwood, Kelly A.
Heys, Caryn Hughes, Peter Lasch, Pierre L. Martin-Hirsch, Blessing Obinaju,
Ganesh D. Sockalingum, Josep Sulé-Suso, Rebecca J. Strong, Michael J. Walsh,
Bayden R. Wood, Peter Gardner, Francis L. Martin,
Nature Protocols 9 (2014) 1771-1791.
Contribution:
I wrote the materials section
As a group, I contributed to other sections of the manuscript
……………………………… ………………………………
Blessing E. Obinaju Prof Francis L. Martin
178
Appendix II
In vitro protective effects of quercetin in MCF-7 cells despite an underlying
toxicity profile
Blessing E. Obinaju and Francis L. Martin
Mutagenesis 27 (2012) 789-816
Contribution:
I collected and presented the data at the 35th Annual Meeting of the United
Kingdom Environmental Mutagen Society.
……………………………… ………………………………
Blessing E. Obinaju Prof Francis L. Martin
182
Appendix III
Attenuated total reflection Fourier-transform infrared spectroscopy
detects real-time polyaromatic hydrocarbon toxicity in fish tissues.
Blessing E. Obinaju and Francis L. Martin
Mutagenesis 29 (2014) 79-96
Contribution:
I collected and presented the data at the 36th Annual Meeting of the United
Kingdom Environmental Mutagen Society.
……………………………… ………………………………
Blessing E. Obinaju Prof Francis L. Martin
185
Appendix IV
Alterations in infrared spectral signature of Heterobrachus bidorsalis
reflects polyaromatic hydrocarbon concentrations in Ovia River, Nigeria.
Blessing E. Obinaju and Francis L. Martin
Mutagenesis 29 (2014) 497-559
Contribution:
I collected and presented the data at the 43rd Annual Meeting of the European
Environmental Mutagen Society.
……………………………… ………………………………
Blessing E. Obinaju Prof Francis L. Martin
188
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